Condition based maintenance is now routinely applied to rotating machinery, with data transmitted wirelessly and reviewed automatically, to give a prognostic or remaining life estimate. The challenge is implementing integrated NDE/SHM strategies for structural assessment. However, advances in computer and communications technology, the internet of things and management of big data are starting to offer the digital infrastructure which can be combined with new sensor systems to enable SHM/prognostics to be applied to structural materials. Some challenges and opportunities which assess and demonstrate how NDE and SHM can potentially be revolutionized in the age of Industry 4.0 are outlined.
On the Value of Structural Health Information
Structural Health Information (SHI) may be provided by various sources such as e.g. structural health monitoring, inspections, testing and digital technologies and networks as by the Industry Internet of Things (IIOT). SHI holds a high potential for industrial, life safety and sustainability value and facilitates an optimal and intelligent life cycle management of infrastructure and machinery systems. The paper documents the utility, decision and reliability theoretical basis for the quantification of the value of SHI – as explicated by the scientific networking project COST Action TU1402 – and the basics of detection and Bayes risk theory, which have shaped the field of structural health monitoring. Both approaches are illustrated with a generic analysis. The value assessment of different SHI types is described and the boundaries for achieving a high industrial and societal value of SHI are explicated.
Statistical Methods for Probability of Detection in Structural Health Monitoring
There is much interest in the potential to use Structural Health Monitoring (SHM) technology to augment traditional Nondestructive Evaluation (NDE) methods to improve safety, increase asset availability, and reduce maintenance and inspection costs. SHM has the potential to be used in many areas of application including critical components in aircraft and pipelines. Probability of detection (POD) plays a critical role in aircraft structural integrity programs. As such, there has been a high interest in developing methods that can be used to assess POD in SHM applications. In contrast to traditional NDE laboratory experiments to assess POD that involve a set of specimens with cracks, SHM sensors are fixed and SHM data are acquired over time as cracks grow or otherwise evolve. Traditional statistical methods for assessing POD (e.g., as described in MIL-HDBK 1823A 2009) no longer apply to such repeated-measures data. This purpose of this paper is to review the basic statistical concepts of probability of detection (POD) and to show how these concepts can and should be applied to SHM POD studies by modifying and extending existing methods for estimating POD. The methods presented here are applicable when there is a scalar damage index or other response that will be used to make a detect decision. The paper compares a simple model based on length at detection and a random effects model to describe repeated measures data.
Quantitative Dynamic and Distributed Fiber-Optic Sensing has Come of Age
While the importance of static fiber-optic distributed quantitative sensing has been already recognized, appreciated and applied, adding full dynamic, real-time characteristics is of crucial importance, as it enables the use of distributed sensing in time-varying scenarios in multiple applications, such as: aerospace, civil engineering, transportation and more. For example, distributed load monitoring and damage detection in a flying platform must have dynamic capabilities due to the constant motion of the subsystems under investigation. Even 'static' applications, such as the distributed mapping of strain over a loaded long wing, require some dynamic performance. Otherwise, the accuracy of the supposedly 'static' measurements may be compromised by parasitic vibrations of the structure. Worse, testing personnel, losing their patience to wait until all vibrations die, will be reluctant to move to fiberoptic sensing. The paper outlines a few promising commercially available technologies for quantitative fiber-optic dynamic and distributed sensing of mainly strain but also temperature, suitable for general SHM applications. It also discusses relevant specifications, with emphasis on the required sampling rates for the temporal bandwidth of a given application and mention a wish-list for a more widespread use of the technology.
Location and Identification of Defects in the Gas Network by the Acoustic Emission Method
Durability, reliability and usability are three foundations of reliable construction, crucial in economic and social context. Because localization and monitor of potential defect and assessment of its impact on condition of the construction are part of the durability assessment, we should develop appropriate methods, which would detect the beginning of technical condition process getting worse and could allow monitoring its progress during exploitation of the construction, not only at areas chosen subjectively or based on unverifiable computer models. One of this NDT methods complied with user expectations is acoustic emission method based on analysis of active destruction processes. The article presents results of AE applications to locate, monitor and identify destruction processes caused by exploitation burdens made to assessment the technical condition of gas infrastructure made from cast iron, steel and plastic.
Real Time Extraction of TOA from Overlapped AE Waves
Time of Arrival (TOA) detection is essential successful Acoustic Emission (AE) based structural health monitoring (SHM) system. During high burst rate results AE waves could overlap. AE waves shows similar narrowband, low frequency content thus overlapping of AE waves leads to overlap in both in time and frequency. As a result, the TOA information is lost in the coda of the previous wave and cannot be (reliably) detected by conventional techniques. In this paper, the problem was addressed through creation of a) Wave (Overlapping) Identification (WI) b) Wave Cleaning (WC) c) Arrival Detection (AD) approaches. In this paper, specifically the Wave Detection and Wave Cleaning approaches are introduced and described in detail. 3D Fingerprinting is meticulously designed to exploit wave arrival characteristics thus autogenously identifying the overlapping of waves for completing the WI system. Positive identification activates Wave Cleaning system which eradicates the influence of the Intersecting Wave using adaptive spectral subtraction (ASpS). Post removal of the TOA for the Intersected Wave was identified.
Observation and Detection of Machining Phases in EDM Microdrilling by Acoustic Emission
Micro Electrical Discharge Machining (Micro-EDM) is a manufacturing process used for drilling of micro-diameter and deep holes whereby electrical discharges emitted by a tool-electrode are used to remove material in a work piece hosed by a dielectric .The process gives the ability to make intricate shapes with great aspect ratio in hard materials not yet feasible with standard drilling process used for instance in turbo-engines. During the process, both the work piece and the tool-electrode get damaged. It is highly difficult and time-consuming to evaluate precisely and in real-time their degree of wear. Moreover, the process is generally managed by a proprietary software which controls both the feed rate of the tool-electrode and the sequence of discharges necessary to remove pieces of material to reach a reference in depth. We demonstrate for the first time that the acoustic emission (AE) technique is able to highlight the different phases in the manufacturing process through the use of an advanced and unsupervised pattern recognition method where each phase is interpreted as an AE source. The task is quite difficult since we are in presence of continuous acoustic emissions on small work pieces. Results concern the drilling of hard material. AE streaming have been collected on multiple AE sensors with different characteristics (for comparison purpose), processed by wavelets for reliable AE hit detection, and then interpreted using a chronologybased consensus clustering with temporal coherence. This is a recent approach used to evidence a chronology of AE sources whereby AE interpretation is focused on the timeline of AE events. The algorithm relies on an unsupervised data fusion process of partitions obtained by many parameterizations (such as subsets of features) with a quantification of both the uncertainty and the robustness of the results obtained. Results show the effectiveness of the approach, in particular in presence of continuous acoustic emissions.
Multi Frequency Acoustic Emission Micromachined Transducers for Structural Health Monitoring
One of the well-established passive Non-Destructive Evaluation (NDE) methods is Acoustic Emission (AE) in which elastic waves produced by sudden energy release of active flaws are detected. Low power consumption and lightweight characteristics along with high sensitivity are important features of AE transducers. Low power and lightweight AE transducers can be developed using Micro-Electro- Mechanical-Systems (MEMS). Unlike the capacitive MEMS transducers, piezoelectric MEMS transducers do not require bias voltage and have lower polarization voltage. Typically, MEMS transducers suffer from low sensitivity. In this paper, piezo-MEMS AE transducers operating in plate flexural mode driven by piezoelectric effect are presented. Piezo-MEMS AE transducers consist of two different frequency transducers tuned to 40 kHz and 200 kHz. Using Piezoelectric Multi-User MEMS Process (PiezoMUMPs) offered by MEMSCAP Co., transducers are fabricated by 5-mask level Silicon-On-Insulator (SOI) patterning and etching process. This process is followed by deposition of 0.5 micron Aluminum Nitride (AlN) which is sandwiched between top (metal layer) and bottom (doped silicon layer) electrodes and functioning as the sensing layer of the transducers. The micro-mechanical layers coupled with the piezoelectric layer are modeled in COMSOL Multiphysics software to understand their performance numerically. To tackle the problem of low sensitivity, two different frequency transducers are connected in series in order to increase the bandwidth and sensitivity. The electromechanical characterization of the transducers is presented. The independent performance of each transducer is compared with the case that they are connected in series to detect the simulated AE events. Taking into account size, power consumption and weight, the designed piezo-MEMS AE device has significant advantages as compared to conventional bulky AE transducers.
Acoustic Emission Sensing using MEMS for Structural Health Monitoring: Demonstration of a Newly Designed Capacitive Micro Machined Ultrasonic Transducer
Among the various experimental techniques used for Structural Health Monitoring (SHM), acoustic emission (AE) allows real time monitoring of large structures with the possibility to detect, characterize and locate damages. In practice, its implementation can be complex, especially for mobile structures. The main difficulties are related to the integration of sensors in the structure, electrical interconnections, different sources of noise, the detection and processing of transient signals and the management of massive data streaming. Generally, AE are collected through piezoelectric sensors. We propose an AEbased SHM methodology using an alternative technology in breakthrough with piezoelectric transduction: Capacitive micro-machined ultrasonic transducers (CMUTs). These sensors have many advantages. One advantage lies in the size of the sensors allowing a little intrusive integration into the material. Following a previous work published in EWSHM’Ì£18, we report on the design, fabrication, and experimental demonstration of a new CMUT transducer specifically designed for the measurement of AE events. A data preprocessing methodology dedicated to interpret the obtained AE streaming is presented. We compare the results with standard piezoelectric sensors to detect damages during tensile tests on composite plates.
Estimation of the Delamination Size of Carbon Fiber Panels from the Impact Sound
Large composite panels of airplanes are regularly inspected, and delaminated sections are repaired. Experienced technicians can detect the delaminated sections of composite panels from the sound created from a small impact. In this study, performance of computerized analysis for impact sound was evaluated for composite panels at different delamination levels. The impact sound of carbon fiber panels without any defect and panels with simulated delaminated sections were recorded. The recorded audio data were processed and interpreted. Fast Fourier Transformation was used for converting the audio data into the frequency (spectrum) or time-frequency domain (spectrogram). Neural networks were used for the identification of carbon fiber panels. Three different approaches were used for encoding the data before they were used for the training and testing of the neural networks. The simulated delamination sizes of the panels were 5mm X 5mm, 10 mm X 10 mm, 20 mm X 20 mm and 30 mm X 30 mm. The Levenberg-Marquardt neural network algorithm provided the most consistent estimation compared to the Gradient Descent with Momentum, Fletcher Reeves and Resilient Backpropagation algorithms. During test cases, the Levenberg-Marquardt neural network algorithm identified a carbon fiber panel’s delamination level with lower than 10% error. The study indicated that the audio data from the sound of impact could be used for the identification of delamination severity in composite structures.
Multi-functional FBG Interrogation for Integrated Structural Health Management
For Aerospace environments, sensor data and sensing systems are commonplace. Aircraft owners depend on those sensors, systems and signals to provide real-time structural health monitoring, supporting operational awareness and decision making, and to predict future behavior. Analysis methods in SHM more and more tend to explore monitoring approaches based on analysis of large amounts of statistical health sensor data. The use of fiber optic technology for sensing was already determined as applicable candidate for sensing technology for the purpose of supporting SHM programs in Aerospace. Enabling factors for operational implementation regards to the degree of structural impact of such systems to aircraft, cost of ownership, and ease of maintenance including the ability to provide prognostic capabilities. The introduction of the integrated photonics technology platform allows an extremely high degree of miniature integrating multi-disciplinary sensing capabilities; A single system footprint enables support and sensing capability for different measurement applications, e.g. damage and impact detection, shape and deflection monitoring, structural load measurements and distributed temperature measure capability, as well as integration of datacom features functions. This paper introduces a fiber optic sensing network system based on integrated photonics technology allowing that versatility. Multiple interrogation functions combined in a single unit, with detectors based on spectrometry and interferometry, combined wavelength and time division multiplexing techniques, high speed data acquisition and solid-state fiber optic channel switching, interrogation of thousands of uniquely identifiable FBG sensors in a single optical fiber. Subsequently, system implementation aspects regarding SHM ambitions are discussed, including sensor network redundancy and operational compliance and reliability requirements for aerospace environments.
Shape Sensing of Beams with Complex Cross-sections Using the Inverse Finite Element Method
This paper presents the iFEM formulation for beams with complex cross-sectional profiles having at most one plane of symmetry. As the iFEM has already been successfully used for the shape sensing of beams with circular or rectangular cross sections, this work paves the way for formulating a generalized one-dimensional beam element, applicable for any kind of cross-section. Challenges arising due to the asymmetry of the profile, in regard to sensor placement on the surface of the beam and accurate prediction of transverse shear strain measures from experimental strain measurements are addressed. The new formulation is illustrated with the specific case of a beam with a symmetric airfoil profile (NACA 0016), subjected to symmetrically and asymmetrically applied static load conditions.
Embedded Fiber Bragg Gratings for the Process and Structural Health Monitoring of Composite Boosters for Space Applications
Within an ESA funded development project, fiber optical sensors were integrated into a dry wound composite booster demonstrator (L=2.5 m, D=0.8 m, t=12 mm) for the Ariane 6 launcher. The aim was to detect the resin flow front during the vacuum assisted infusion of the carbon fiber preform. Furthermore, the strains during the subsequent burst test were measured with the same sensors. Therefore 16 optical fibers with 98 inscribed fiber Bragg gratings (FBG) were manually placed into the preform within specific layers and positions. The optical fibers were successfully guided through the infusion and vacuum assembly and connected to a Micron Optics Si255 interrogator which was mounted directly at the mandrel. During the infusion and curing process the demonstrator tank and the entire measurement equipment rotated in an oven and was heated up to a temperature of 125° C during curing. Therefore a special air cooled box was designed to keep the equipment’s environmental temperature below its maximum operational temperature of 60 °C and thus guarantee its function during the whole process. The gained resin flow front data was compared with an infusion simulation carried out by the Institut für Verbundwerkstoffe GmbH, Kaiserslautern. The comparison of the results showed the potential of this measurement method for the flow front detection in thick-walled laminates. Following the manufacturing, a burst test of the demonstrator was performed. The strains were measured with the same FBGs while the demonstrator was pressurized with water until its burst pressure of approx. 450 bar.
Elliptical Triangulation Method for Damage Identification on a Complex Aeronautical Structure
Composite structures are very appealing in aeronautic applications due to their unique stiffness to mass ratio but, due to high sensitivity to damages and defects, their use is still limited to oversized structure. For this reason, in the last years, interest in Structural Health Monitoring (SHM) has increased exponentially. The process of monitoring in real-time a structure, referred to structural health monitoring, consists of several steps: damage detection, localization, classification, and quantification. Few non-destructive guided waves-based techniques have been developed. The damage detection method used in this paper is the elliptical triangulation one, which is based on the propagation of ultrasonic waves in each sensing paths. The test article under investigation is a winglet made of fabric laminate of fiberglass infused in epoxy resin and foam coring. In view to detect damages, the winglet has been equipped with piezoelectric sensors, which act both as sensors and as actuators. A database at the unknown (and possibly damaged) state is compared to a healthy reference database. The elliptical triangulation method has been applied to the recorded signals and results, in terms of damage positioning and detection of the damaged area, have been assessed. Additional information about the damage paths have been verified by using the damage indexes.
Strain-based Structural Health Monitoring: Computing Regions for Critical Crack Detection
The development process of aircraft structures requires many fatigue tests. During these tests, engineers have to inspect the aircraft structure continually to avoid final fracture of any component. Structural health monitoring may reduce time and cost associated with these inspections. Evaluating applied strain sensors is one possibility to monitor the structure. As applied strain sensors do not contribute sufficient information in every location for crack monitoring, test engineers have to position them carefully. Only in appropriate positions, strain sensors provide data that may be analyzed to detect crack growth before criticality, including scenarios with multiple cracks and different lengths. This paper presents a method to compute and maximize regions of appropriate positions for strain sensors on a test structure. The resulting detection regions may be used for appropriate positioning and minimizing the number of required strain sensors, while ensuring detection before criticality. The proposed method exploits the fact that growing fatigue cracks change the strain field in the structure. Therefore, the relative change of strain amplitude under periodic loading will serve as detection measure. Once it exceeds a specified critical threshold value that is linked to sensor sensitivity, final fracture is assumed to occur. Appropriate detection regions for strain measurement are computed in three steps: First, the space of crack configurations, each represented by a crack length vector, is probed by a Monte- Carlo sampling scheme. For every configuration, criticality is assessed based on stress intensity factors using a linear Finite Element Analysis. Second, a mathematical substitute model is trained using the sampling data. The result is a fast mapping from the crack configuration space onto the space of stress intensity factors. Third, for all critical crack configurations, the regions of critical detection measures are assembled into a global detection map. This map includes regions where detection is possible before a critical crack configuration occurs. The approach is applied to a demonstrator that resembles the section of an aircraft structure exhibiting two fatigue cracks. An extension to many cracks is discussed.
Mobile Robotic Platform for Inspecting Aircraft Surfaces Using Lamb Waves
Due to their unique material properties and high strength-to-weight ratios, composites are an attractive choice over traditional isotropic materials for use in aerospace. However, composite structures are susceptible to failure modes that are not readily identifiable by visual inspection. This raises concerns over maintaining flight safety throughout the life of an aircraft with high proportions of composite structure. Hence, there is a need for an autonomous method that allows for damage detection in composites while reducing inspection time and cost. This paper introduces the concept of automated nondestructive testing (NDT) using Lamb waves on a mobile robotic platform to detect structural features, such as edges and ribs, in addition to any defects. The robot is envisioned to perform simultaneous localization and mapping while performing ultrasonic NDT. Presented in this paper are the potential capabilities of a prototype mobile inspection robot as well as a novel method for Lamb wave-based edge-detection that results in the map of a flat surface. The latter approach is successfully applied to a numerical example as well as data from laboratory experiments.
Real-Time Impact Identification by Using Acceleration Measurements—Application to a Large Aircraft Composite Panel
This study presents a new impact identification technique that can simultaneously localize an impact event within a complex structure and assess its severity. The proposed approach is based on a rigorous definition of the impact point signature in the vibration response. An impact test campaign is performed on an A350 aircraft crown panel equipped with a network of eight accelerometers. Results show that a single accelerometer can successfully localize 88% of the applied impacts. The use of all the accelerometers increases the robustness of the proposed technique.
Experience with On-board SHM System Testing on Small Commuter Aircraft
The general SHM idea which promises significant benefits for new designed vehicles, buildings and structures is simple and clear but its transition into reality brings many questions which needs to be answered. Monitoring of aircraft structures is one of specific SHM applications which puts several requirements on SHM system design like minimal impact on the aircraft design and manufacturing, low weight and small dimensions, system modularity and installation versatility, long-term and automated system operation, and integration with airframe and avionics. Stated requirements have been incorporated in the SHM system design described in this paper. This SHM system is an automated on-board system based on Ultrasonic Guided Waves (UGW) technology. It has been developed for the application on commuter aircraft. Specifically, this system has been installed on the flying prototype and on the full-scale fatigue test of the wing and central part of the fuselage of L 410 NG aircraft. The main intent of this paper is to present experiences gained during the SHM system operation on both the flying prototype and the full-scale testing. Installation and operation of the SHM system on the flying prototype have brought invaluable experiences related to integration, operational conditions and limits. Such experiences are important for future development of the HW part of the SHM system and for mitigation of environmental conditions influence on measured signals. On other hand, the SHM monitoring on the full-scale test proved capability of the system to cover all Principal Structural Elements (PSEs) prone to fatigue damage defined by OEM. This test is very important for evaluation of detection capabilities of the system and its comparison to common GVI and NDI methods as well. Additional experiences acquired from this SHM testing are related to measurement, collection, transfer and evaluation of huge amount of signals. This knowledge will be crucial during a SHM operational concept development and SHM technology implementation into service.
Sensor Integration and Data Exploitation of Structural Health Monitoring Network Integrated on a Unmanned Aerial Vehicle (UAV)
Unmanned Aerial Vehicles (UAVs) are flying robots that require little or no human control while flying. The number of UAV has growth exponentially due to rise in applications such as video taking, surveillance and delivering cargo to customers. Despite the promise of the technology, UAV are not fully reliable currently, either civil or military. They lack of sensing mechanisms to detect issues in operation, such as impacts, hard landings and overload, together with the difficulties to detect structural problems during the UAV operation could compromise not only the safety of the UAV, also all along the entire flying area. This paper presents the design, development implementation, and validation of a Structural Health Monitoring (SHM) system applied to the rear fuselage of the UAV developed by INTA for R&D activities (MILANO). The rear fuselage, a 2.5 meters carbon/epoxy structure with frames and stringers, was instrumented with fiber optic sensors and PZT. Two different types of fiber optic sensors (FOS) were considered: Fiber Bragg Gratings (FBG) and distributed fiber sensing based on Rayleigh backscatter. The objective of the fiber optic sensor network is to detect the changes induced on the strain field due to damage appearance. Different algorithms, such PCA and Artificial Neural Network (ANN) will be discussed. Validation will be performed with measurements on a real structure with increasing damages by removing rivets progressively at different load levels. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Networks (RNN), will be proposed for accurate damage detection with the FBG sensing network in real time operations.
Methods for SHM System Capability Evaluation from Engineer’s Perspective
Usually, the capability of Structural Health Monitoring (SHM) systems using Ultrasonic Guided Waves (UGW) is evaluated based on the Probability of Detection (PoD) curves. These determine the detectability of structural damage (e.g. a fatigue crack) of various size, just like in in other NDT testing methods. Such approach, although rigorous, provides fairly complex procedure to engineers in practice and is hard to follow. In this contribution, an approach more connected with engineering practice, is presented. It should be however noted, that although more practically focused, the presented method does not mean the design engineer would avoid using a sample structure to simulate the structural damage and transfer it, by technical similarity, on the real product. First, a traditional approach of PoD evaluation, is described and analyzed. An approach for evaluation of the PoD of individual damage size is presented- evaluation of a batch of repeated measurements enable the user to determine the variation of measured signal in terms of Difference index (DI) used (further referred to as the self- DI). Using the Central limit theorem enables then to determine not only the mean value of the self-DI, but also its variation regardless of its real distribution. Then, after imposing damage to the structure and performing the UGW measurement again, the user obtains the signal difference with respect to the first measurement with no damage (further referred to mutual-DI; additionally we obtain the information of the measurement system stability by comparing the first and second measurement self-DI mean and variation).
Inspection Requirements for Continued Airworthiness Using Structural Health Monitoring
Aircraft structural components are subject to fatigue resulting in damage initiation, propagation, and subsequent catastrophic failure due to unstable damage growth. Current aviation regulation for inspection requirements to maintain continued airworthiness for fatigue critical components are based on non-destructive inspections (NDI) with a deterministic damage tolerance approach for fail-safe aircraft structures. Recently, Structural Health Monitoring (SHM) technology has shown great potential to improve aircraft safety, increase aircraft availability and reduce maintenance and inspection cost. However, SHM systems and traditional NDI systems exhibit different performance behavior from a probabilistic point of view. In this paper, we propose and demonstrate a probabilistic approach to determine the inspection frequency for a SHM system to achieve the same reliability level in inspection requirements for continued airworthiness as for a traditional NDI system.
Experimental Study on the Measurement of Dynamic Strains for Composite T-Joint Subjected to Hydrodynamic Ram Using PVDF Sensors
Hydrodynamic ram (HRAM) is a type of combat damage that occurs when a highspeed projectile passes through the fluid in the structure and explodes. Since the vibration of structure due to HRAM includes high frequency components, sensors for measuring dynamic strain signals having wide frequency bands are needed. In this paper, an experimental study was performed to demonstrate the feasibility of using a PVDF sensor to monitor the dynamic strains of a composite T-Joint subjected to HRAM pressure. The PVDF sensor calibration test was performed to determine the resistance and capacitance for the entire circuit of the measuring equipment. An HRAM test of a composite T-Joint was performed using a ram simulator which can generate HRAM pressure. The failure behavior of the T-Joint due to HRAM pressure was measured using a PVDF sensor and strain gauges. The dynamic strain measurement and damage detection of structures subjected to HRAM using a PVDF sensor were investigated.
Feasibility Analysis of Various Sensing Methods for Nondestructive Testing of Composites
Guided ultrasonic wave-based methods are promising for detecting defects over long ranges in isotropic and composite materials. The technology has seen a lot of attention in the research community over the past decades, and many analytical and numerical methods have been developed to describe different aspects of wave propagation and scattering phenomena. However, very little research was geared towards the physical implementation in practical applications. Hence, in this study, the detectability of defects in composite materials (delaminations and core-skin disbonds) with different sensor technologies is investigated. To induce and record scattered guided ultrasonic waves, broadband contact transducers, air-coupled transducers and a laser Doppler vibrometer are used. It is shown that the thickness of typical sandwich panels used in aerospace industry and the non-homogeneous nature of the honeycomb core material make precise localization and identification of defects challenging, in particular in non-contact scenarios.
Full Field Reconstruction in Aeronautical Structures Subjected to Aerodynamic Loads by a Matrix-Based Load Monitoring Algorithm
A method for reconstructing the strain (and stress) field of the entire structure based on an equivalent set of lumped forces representative of the real boundary load condition, correlated with strain measures in discrete positions, is adopted. The method, namely the Calibration Matrix approach, is based on a least-squares minimization of an error functional defined as a comparison between discrete strain measures and a numerical formulation of the same. By assuming a linear relationship between strain and forces, the minimization process can be performed analytically, leading to a computationally very efficient algorithm that can be operated in real time and without requiring an aerodynamic training set to be defined. The method is numerically tested on a full-scale UAV wing subjected to different aerodynamic pressure loads. The results confirm the method ability to correctly reconstruct the strain (and, thus, the stress) field due to an aerodynamic pressure through an equivalent set of lumped forces, automatically adapting to different load scenarios
The Use of Adapted Measurement Techniques for the Structural Monitoring for Ariane Group Needs: Focus on Two Specific Needs and Solutions Evaluated
ArianeGroup has been looking for solutions and advantages of Structural Health Monitoring (SHM) for launchers since a long time. Development and strong improvement of new sensing technologies, with also the reusability objectives, brings new possibilities and interest for SHM. In that frame the presentation will focus on two main problematics for the monitoring of our structures and on the solution developed. A brief review of two approaches for SHM will be present: adapting enhance NDE and using the mechanical behavior approach; each with the technical solutions evaluated.
Concrete Culvert Bridge Condition Monitoring Using Acceleration Responses: A Case Study
Vibration-based structural health and condition monitoring has become one of the main methods for monitoring changes, in particular damage in a structure on a global scale. However, it has not been widely used for identifying condition change of the structure itself. This paper demonstrates the feasibility of utilising acceleration responses measured from a short-span concrete culvert bridge located in New South Wales, Australia, to identify the effects of structural condition changes due to bridge widening. The frequency-domain decomposition (FDD) technique was applied on the acceleration data, from which the decrease in modal frequency was observed caused by the event of bridge expansion, demonstrating the capability of FDD for identifying the change in natural frequency due to bridge structural change.
Autonomous Application of Smart Cement Sensor-repairs
Self-sensing cements offer a unique opportunity to unify our approach to the remote monitoring and maintenance of concrete assets. The objective of this work is to develop a remotely-deployable, “smart cementâ€ technology that can simultaneously repair damage, monitor underlying structural strain and attempt to detect further defects. Automating the deployment of these materials using robotics will improve the efficiency of installation and permit application to areas with restricted access or hazardous environments. Here, we present our latest progress in manufacturing and robotically spraycoating smart cement sensor-repairs onto concrete substrates. Spray-coating provides controllable, high pressure distribution of the material, providing high geometrical accuracy in addition to high adhesion. The smart cements are based on metakaolin geopolymers — a novel class of cement-like binders that cure to a strong, chemically resistant, electrically conductive shell. In this work, the manufacturing process of the geopolymer patch is presented before investigating the sensing capability. Initial results regarding the temperature and strain sensing of the patch demonstrate the dependence of measurements on the frequency of the applied potential. Furthermore, exploiting this dependence presents the possibility of self-compensating for temperature.
Experimental Verification of the Eddy-current Inerter System with Cable Bracing for Seismic Mitigation
In this study, a novel vibration mitigation device which uses a displacement amplification mechanism is proposed. It is called cable-bracing inerter system (CBIS) and provides the additional damping force through inerter element, friction element and eddy current damping element. Eddy current damping is a non-contacting damping mechanism, and the damping ratio can be easily adjusted by varying the air gap between the permanent magnets and the conductor in this device. In the traditional tuned mass dampers, large additional mass is often required for its seismic control which is a limitation of a tuned mass damper. This device overcomes this kind of limitation and its effective masses can be several times than the actual mass. In this paper, we present a comprehensive study that involves experimental, analytical, and computational approaches. First, we described the principle of the CBIS which includes eddy current damping mechanism and an apparent mass amplifier using inerter. CBIS utilizes two cables which are easy to install to transmit control forces and deformation between the main structure and CBIS. Second, the theoretical model was given and to simplify the theoretical model of CBIS, the flexibility of the cable was neglected and the motion governing equation was also given. A series of free vibration tests and shaking table tests were conducted on a single degree of freedom (SDOF) steel-frame model with/without a CBIS to evaluate the effectiveness and performance of the CBIS in suppressing the vibration of the model. In the free vibration tests, the extend Kalman filter is used to identify the parameters of structure and CBIS. The results show that by using a properly designed inerter system, a lightly damped primary system can achieve a considerable reduction in its response with a small weight penalty. The experimental results show that the CBIS can effectively reduce the displacement and acceleration responses under different earthquake excitations.
Application of Image Recognition Technology to Sound Spectrogram of Impact-Echo Method
In this paper, as a part of the comprehensive deterioration evaluation of the concrete structure, a method of evaluating the impact sound by machine learning was studied. The recorded impact sound data are converted into spectrograms, and the images are recognized by supervised learning, whereby the state of the defect in concrete. With concrete test specimens having voids in different depth (from 35 to 80 mm) and diameter (from 80 to 350 mm), the spectrograms of the Impact-Echo sound are taken and converted into grayscale pictures in different frequency-time range and resolutions. Neural networks were created with the grayscale images with supervised learning on the specification of voids, and the estimation results are evaluated. It was confirmed that the image recognition of spectrogram of Impact-Echo sound by machine learning could possibly estimate the state of the defects in concrete. Also, adequate specification of frequency-time range and image resolution were examined and discussed.
Geothermal Power: Monitoring the Building Response During Installation
The European project G E O F I T (Deployment of novel GEOthermal systems, technologies and tools for energy efficient building retrofitting) is gathering more than 20 partners from all around Europe. Its main objective is to deploy and to integrate advanced methods of worksite inspection, ground research, and building structural monitoring, drilling and worksite characterization into advanced geothermal based retrofitting methods. This contribution reports the experimental results to be achieved within GEOFIT at the location of specific case studies. In particular standard accelerometric measurements will be collected and compared with the information collected by the geo-radar system made available by one of the partners. This paper focuses on the structural monitoring of a two-story masonry building. The results of a preliminary data collection in the absence of drilling are also reported.
A New Approach to System Identification of Tall Buildings
The common approach for system identification of multi-story buildings from vibration records is to use modal identification. The identification involves identification of the frequencies, damping ratios, mode shapes, and the participation factors of each mode. Modal identification can give misleading results if the building does not have modes in classical sense (i.e., the damping matrix is not proportional to mass and/or stiffness matrices), such as buildings with nonlinear behavior, nonproportional damping, and strong vertical irregularities (e.g., sharp changes in mass, stiffness, and damping along the height). Transfer-matrix formulation of the dynamic response of multi-story buildings provides an alternative to modal analysis. In transfer-matrix formulation, the building is considered as the superposition of one-story structures (i.e., one-story structures placed one on top of another). The dynamic response of each story is calculated separately, in terms of its own story mass, stiffness, and damping by accounting for the forces and displacements from the adjacent stories above and below. The transfer matrices define the relationship between the forces and displacements above and below the stories. We show that we can identify the individual natural frequencies and damping ratios of each story (i.e., as if each story were a one story structure), provided that we start the identification from the top story. It can be shown that the top-to-bottom spectral ratio of a story is influenced by the properties of all the stories above, but not the stories below. This allows detecting the location of the damaged story. One requirement for the transfer-matrix based identification is that we need records from every story. It can be shown that by approximating the deformations of a building as a linear combination of nonlinear (i.e., time and frequency dependent) shear and bending beams, we can get a very good estimate of the responses at noninstrumented floors from those of the instrumented floors.
On the Utilization of Synthetic and Measured Earthquake Ground Motions for Designing Building Monitoring Systems in the Near-field of Major Faults
Agencies and research groups engaged in studying measures for enhancing the resiliency of communities have recently placed emphasis on the need for extensive implementation of monitoring systems for rapid post-event assessment of structural integrity. Designing a monitoring system for a building requires a thorough knowledge of its potential nonlinear dynamic behavior with an associated localization of interstory drift. Extending this task across a regional scale becomes even more challenging because of the heterogeneity of the buildings inventory and the limited knowledge of the characteristics of the demand especially for sites located in the near-field of a major fault. The existing observational database of near-field ground motion records is in fact too limited to constitute a comprehensive basis for full understanding of the potential range of structural response variability at different locations near a major fault. In addition, current insight into monitoring system design typically relies on linear structural models and sensors deployed on a limited number of floors. In this context, this paper presents first results of a study that combines synthetic earthquake ground motions generated from a massively parallel regional-scale geophysics wave propagation model at frequencies of engineering interest (0-5 Hz) with nonlinear tall building models. The objective is to gain new insight into the potential impact of localization of nonlinearities in structures subjected to realistic near-field earthquakes and develop a methodology that optimizes the deployment of sensors at the building and site level. In addition to the large database of synthetic motions, available real records are also employed to compare and contrast with the trends observed using synthetic ground motions.
Fabry-Perot Fiber Optic Sensors for Civil and Geotechnical Monitoring of Large Structures
Miniature optical fiber sensors, based on Fabry-Perot technology, have found numerous applications in both patient monitoring and civil structural monitoring of large and massive infrastructures. Although structural health monitoring and patient monitoring may benefit from the unique advantages of optical fiber sensors (OFS) such as electromagnetic interferences (EMI) immunity, sensor small size and long term reliability, both applications are facing very different realities. This contribution presents the underlying sensing technology, the miniature sensor fabrication technique and numerous application examples in medical and civil engineering-fields. OFS for medical applications are single-point, measuring mainly parameters such as pressure or temperature. In the intra-aortic balloon pumping (IABP) therapy, a miniature OFS can monitor in situ aortic blood pressure to trigger catheter balloon inflation/deflation in counter-pulsation with heartbeats. Similar sensors reliably monitor the intracranial pressure (ICP) of critical care patients, even during surgical intervention or examinations under medical resonance imaging (MRI). Temperature OFS are also the ideal monitoring solution for such harsh environments. The same miniature sensing elements can be packaged differently to allow their use in civil and geotechnical monitoring. In particular, Fabry-Perot optical sensors have been in use for many years as direct replacement of conventional sensors based on the vibrating wire and other electrical technologies. Those sensors are ideal to measure strain, deformation, pressure and temperature in applications subject to strong electromagnetic fields, lighting strikes or requiring long cables. Application examples includes the monitoring of groundwater pressures in tailing dams used for mining applications in Chile, leaks in levees in The Netherlands and displacements in the cryogenically cooled superconducting magnets for ITER project in France.
Estimation of Remaining Life of the Orthotropic Deck of a Bridge Exposed to Extreme Traffic and Wind Actions
Design rules in normative documents provide additional capacity to structures by using safety coefficients and considering the worst cases of loads that in most cases never occur during the life of a structure. This extra capacity allows not only to stay on a safe side during the design operational period of structures but also to re-estimate and to extend their possible operational life. One of the most appropriate ways to estimate the remaining life of an existing structure is the extrapolation of monitored actions or load effects in critical details. The main objective of the current study is the assessment of the reliability of the steel orthotropic deck of a viaduct at the end of its design life based on limited data for traffic and static wind actions. The object of study is the Millau viaduct, a cable-stayed bridge located in Southern France, which is exposed to both, heavy traffic loads and wind loading among other actions. Monitoring data used for traffic are provided from the bridge weigh-in-motion system and include six months of data for the more loaded side of the bridge. Wind velocity and directions for the same period are taken from the structural health monitoring system of the viaduct and the nearest weather station. The methodology is based on fitting the generalized Pareto distribution to extreme stresses induced by traffic actions and by wind in order to obtain return levels at the end of the 120 years design live. Reliability indexes for the most critical part of the deck are calculated for both types of load effects separately and for their combination. Moreover, the comparison between models based on data from monitoring and on European standard models is shown.
Detection of Damage-induced Fatigue Response Based on Structural Health Monitoring Data of In-service Steel Bridges Using Artificial Neural Network
Successful signal-processing of the collected SHM data can provide information for early detection of the structural performance variation, which can indicate damage or deterioration. Fatigue assessment is a critical component of condition assessment for inservice bridges, especially steel bridges with fracture-critical elements. In addition, at an early stage damage, the measured damage-induced fatigue response can be misinterpreted as the other measured deviant fatigue responses. This study aims to develop an Artificial Neural Network (ANN) model to predict the normalized fatigue response over a discrete period of data collection at the healthy condition. The proposed ANN model is used for a case-study vertical lift truss bridge, the Memorial Bridge, located in Portsmouth, NH. A 12-month data collection period, from the long term SHM program of the bridge, is used to investigate the variability of the traffic pattern and the impact on the measured fatigue response. Through a validated global finite element model of the bridge, a physical damage case is simulated to compute the time-history stress response. The computed numerical stress response is applied to evaluate the capability of the ANN model in recognizing the measured fatigue response in the damaged conditions.
Incorporating Non-destructive Field Measurements in Stochastic Finite Element Analysis of Reinforced Concrete Structures
Unlike deterministic analyses where the sensitivity to material inputs cannot be captured, stochastic simulations with the addition of established material distributions and damage prediction models can provide a higher level of confidence in structural behavior. In this study, the use of stochastic analysis as a way to better simulate the uncertainty effects often encountered in structural evaluation was investigated. Stochastic analysis capability was implemented into VecTor2, a nonlinear finite element analysis program for reinforced concrete structures, and simulations were performed on a series of reinforced concrete beams and shear walls. The simulations were compared against deterministic and experimental results and found to be adequate substitutes whilst providing a level of confidence consistent with each specimen. To achieve narrower confidence intervals, early stage field measurements were incorporated in the stochastic analysis. Using early stage measurements such as deflections, crack widths, and reinforcement strains, the stochastic output was recalibrated to produce more accurate results.
Bayesian Modeling Updating of Miter Gates with Uncertain Boundary Conditions
Navigational locks are critical infrastructure components; however, traditional inspection of the lock components, requires closing and dewatering the lock to perform visual inspections, which stops fluvial traffic and consequently presents significant economic losses. Moreover, the gates are typically the critical component in the navigational locks; due to impact or environmental deterioration, a gap occurs in the quoin between the gate and the wall, then the gap produces redistribution of the stresses that are rarely considered during the design of the gates, and this modification in the response accelerate the deterioration in some components of the gate. To overcome this challenge, previous researchers have presented structural health monitoring strategies for miter gates, for example, automated damage detection using strain gage measurements based on principal component analysis have been proposed. In this study, Bayesian model updating of miter gates with uncertain boundary conditions is presented with the following details: the uncertain boundary conditions are represented by the gap profile in the boundary with random depths at discrete locations, the slope of the strain gage measurements with respect to water levels is used as a representative damage feature, and a detailed finite element in Abaqus of the gate and the support is considered, where the gaps are modeled as nonlinear gap elements. Due to the large computational time required for the finite element model used, transitional Markov chain Monte Carlo simulation is implemented to sample the model parameters in high probability regions, from which statistical estimators are obtained. The results demonstrate the potential of the approach to perform Bayesian-based damage prognosis using digital twins.
Monitoring of Membrane Structures using Unmanned Aerial Vehicle Assisted Photogrammetry
High-value assets demand rapid inspection and structural assessment methodologies to ensure maximum availability and operational capabilities. In the case of large structures (e.g. aircraft wings, dams, vertical tanks, elevated highways), the ability to add non-contact measurement capabilities to unmanned aerial vehicles (UAV) has resulted in new options in structural health monitoring and assessment. Large floating covers over anaerobic lagoons in wastewater treatment plants are a type of significant structure that will benefit from the development of this form of non-contact technique. Large membranes are used as floating covers at the anaerobic lagoons at Melbourne Water’s Western Treatment Plant (WTP), Werribee, Victoria, Australia. A critical function of this high value asset pertains to the harnessing of the biogas gas emitted by these lagoons. Without these covers, the biogas will be released into the environment as a damaging greenhouse gas and the opportunity to generate electricity from the biogas will be lost. Therefore, a proactive inspection and management strategy is required to ensure the integrity of the cover and its continued operation. Demonstration of the UAV assisted photogrammetry surveys conducted over the floating cover at the WTP will be the subject of this paper. The results from monthly surveillance of the covers will be discussed. It will be shown that this is a proactive technique that facilitates quantification of the state of deformation of the floating cover due to the dynamics under the covers. These dynamics are attributed to the inflow of the raw sewage, accumulation of bio-gas, “scum-bergâ€ formation and its movement, the effects of wind or a combination of these factors.
Predicting Fatigue Life of Miter Gate Anchorages with Stochastic Modeling and Limited Sensor Data
The US Army Corps of Engineers (USACE) maintains a large inventory of lock and dam structures along inland waterways in the US. Miter gates are key components of lock systems and therefore the ability to assess the fatigue life of miter gates is critical to direct maintenance operations and prevent costly disruptions to commercial traffic. Miter gates are supported by steel anchorages, which transfer the gate overturning reaction to the concrete lock wall. Many anchorages have been in service for 80 to 90 years and may be approaching the end of their fatigue life. However, their condition is unknown because they are embedded in concrete. We present a new method that addresses four major challenges for predicting fatigue life of miter gate anchorages: (1) estimating loads on the anchorage; (2) computing stress ranges in the anchorage based on those loads, accounting for steel-concrete interaction; (3) estimating the number of load cycles on the anchorage; and (4) incorporating uncertainty and the ability to update estimates based on inspection data. The goal of this method is to predict anchorage fatigue life without strain gage data. It is prohibitively expensive to install instrumentation on every anchor in the USACE inventory, and therefore the limited available instrumentation is used to verify the proposed methodology. Using this framework, fatigue life estimates are made using S-N curves. Ultimately, the deterministic fatigue life predictions will be incorporated in a stochastic model that accounts for uncertainties in loading and modeling assumptions.
An Expectation-maximization Algorithm-based Framework for Vehicle-vibration-based Indirect Structural Health Monitoring of Bridges
We propose a vehicle-vibration-based indirect structural health monitoring (SHM) framework that uses acceleration signals collected from within a moving vehicle to identify global modal and structural parameters of a full-scale and in-service bridge. Motivated by many benefits of indirect sensing methods, such as low-cost, low-maintenance and no interruption to traffic, researchers have in the past presented different algorithms and evaluated them on several simulation and lab-scale datasets. However, the uncertainties of the real-world vehicle-bridge interaction system and limited training data may cause previous methods to fail on full-scale bridges. To address these uncertainties, we 1) cast the vehicle-bridge interaction system as a linear time-varying Gaussian statespace model, which is not only able to estimate unobserved bridge responses but also able to add a stochastic process for modeling uncertainties, and 2) propose a hybrid algorithm that uses non-linear least squares and the expectation-maximization algorithm to estimate modal and structural parameters of the bridge using partially observed data (only the vehicle’s dynamic response is observed). We conducted field experiments on a steel truss bridge carrying two rail lines across the Monongahela River in Pittsburgh, Pennsylvania. For estimating the damage that is simulated by placing stationary trains on the bridge, our proposed approach has a 36.3% error reduction compared to a fully data-driven method. The results show that our proposed algorithm provides a potentially practical approach for continuous monitoring of in-service bridges.
Distributed Fiber Optic Sensors for Multiple Crack Monitoring in Reinforced Concrete Structures
In this paper, a multiple cracks monitoring system composed of a fiber optics cable and an interrogation unit based on Optical Backscattering Reflectometry technique is demonstrated. A mechanical strain transfer model is validated by performing an experimental test on a real scale reinforced concrete beam. The model is fitted to the strain spatial distributions, and the resulting parameters are discussed. Estimated crack openings are compared to those measured using traditional LVDT sensors and Digital Image Correlation (DIC) technique. Results show that this system, initially a strain monitoring system, can efficiently detect all multiple cracks, follow its propagation inside the structure, and more importantly monitor the surface and in-depth crack openings. The proposed multiple crack monitoring system will be of great help for early crack detection, as well as monitoring long-term degradation phenomenon like fatigue.
Research on New Type Disc Lock Scaffold Safety Monitoring System
With the continuous development of building and bridge engineering, building formwork system in our country has experienced the development from wooden formwork to steel formwork and then a new type of formwork system at the present stage. As an important part of formwork system, its support system has also experienced the development from wooden bar or steel pipe to formwork made of steel or wooden beams and then scaffold support system. However, some problems in the use of traditional scaffolding system promote the emergence of a new type of disc lock scaffolding support system, which makes up for the shortcomings in the performance of traditional scaffolding. In this paper, a safety monitoring system for the new type of disc lock scaffolding is studied and applied to practice Through the monitoring of stress, incline，settlement and displacement of the scaffold，its safety can be ensured. Based on the monitoring data，some conclusions have be drawn and the direction of the research can be clearer.
The Effect of Truck Loads on Highway Bridges via Bridge Weigh in Motion Algorithm
Traffic loads on highway bridges have been increasing in the past years. It is necessary to quantify the actual truck axle configurations and weights for monitoring the live load and its effect on the bridge conditions. Bridge Weigh in Motion (B-WIM) systems typically employ instrumentation installed on the bridge to measure the speed, axle configurations and weight of crossing trucks. Instrumentation should also be included to measure critical strains, distortions and accelerations due to both the static and dynamic effects of any truck crossing a bridge. Once truck live loads and the corresponding critical bridge responses are identified, it should be possible to correlate and interpret this data to estimate whether continued truck crossings are causing significant changes in the lifecycle of a bridge. A prestressed concrete highway bridge was instrumented with both a bridge monitoring system and a pavement weigh-in-motion system. The structure was identified, and a finite element model was calibrated to allow B-WIM and bridge vehicle interaction analysis. The authors implemented the traditional Moses’s static B-WIM algorithm with the influence line generated from the calibrated model. The example illustrates the challenges and limitations of such approach.
Structural Durability Monitoring of a Newly Built High-Piled Wharf in a Harbor with Anode Ladder Sensor
Structural health monitoring (SHM) of civil infrastructure has received significant public attention in recent years. However, there is currently little research on the durability-monitoring technology of high-piled wharfs in coastal ports. The benefits of anode ladder sensors are their small size, good durability and long life. Based on the properties of high-piled wharfs in coastal ports and the benefits of anode ladder sensors, the structural durability monitoring (SDM) system for a high-piled wharf in the Tianjin Port of China is devised and deployed partly using the anode-ladder sensor. The system can monitor the structural durability of reinforced concrete, which provides a state-ofthe- art mean to monitor the steel corrosion conditions for the wharf. In total, 2 anode ladders are installed in the reinforced concrete components of the back platform and front platform. After the installation of some components in the wharf construction site, the good signal that each sensor measures demonstrates the suitability of the sensor setup methods, and it is proper for the full-scale, continuous, autonomous SDM deployment for the high-piled wharf in the costal port. The South 27# Wharf SDM system is a typical engineering application case of wharf structures in costal ports. This deployment demonstrates the strong potential of SDM to monitor the corrosion state of large-scale coastal wharf structures. This study can provide a reference to the long-term durability-monitoring system deployment for high-piled wharf in coastal ports.
Field Assessment of Ambient Vibration-Based Bridge Scour Detection
Scour is the main cause of bridge failure in the United States and around the world. It involves the loss of soil from around bridge foundations due to fast flowing water. Monitoring bridge scour is challenging as it occurs underwater. A scour monitoring technique that uses accelerometers on the bridge piers and deck to provide real-time measurements of ambient vibration, from which scour-induced changes in the natural frequencies of the bridge may be monitored, has recently emerged. However, field validation of this method is difficult as scour is hard to predict – a monitoring system would need to be installed on a candidate bridge with no guarantee that natural scouring would occur within the trial monitoring period. Previous research on vibration-based scour monitoring has therefore been limited to laboratory-based experiments and numerical simulations. This paper presents a field study in which a bridge with preexisting scour was monitored throughout a repair program, which involved the controlled raising of the riverbed level (i.e. scour in reverse). Ambient vibration due to vehicle traffic over the bridge was monitored before, during and after the repair, using eight high-sensitivity accelerometers and a remote data acquisition system. The riverbed profile was measured with sonar scanning before and after the repair. Numerical modeling predicted a 3-5% change in the first two natural frequencies of the bridge due to the scour repair. However, the actual frequencies measured on site, derived using operational modal analysis (in this case, Frequency Domain Decomposition), showed a high degree of variation. Alternative structural response parameters (mean power spectral density at the modal peaks and the modal amplitude) were found to capture the change in behavior resulting from scour. The opportunities for using these alternative parameters as a measure of scour, and the challenges involved in using operational modal analysis for scour monitoring on a real bridge, are discussed in detail.
Damage Detection and Localization in Concrete Slabs Through Linear and Nonlinear Vibration Analysis
In this study, different alternatives to detect, localize damage from linear and nonlinear vibration responses are investigated. These alternatives are compared on two concrete slabs which were subjected to two different damage types and extents. Linear and nonlinear modal test configurations were conducted. The change of linear resonant frequencies and the nonlinear behavior with damage are compared. Then, the modal shapes are leveraged for localizing the damage position in three different ways. First, the coordinate modal assurance criterion was used. Second, a local damage index is defined as the difference between the maximum principal curvatures (before and after damage) of a set of resonant modes. Third, a nonlinear based localization is proposed by weighting the modal shape curvature differences by its corresponding nonlinear parameter. The results show that linear based techniques were able to localize the damage position in the first slab (severe damage), but failed in the second one (subtle damage). The proposed nonlinear localization technique was able to detect and localize the damage position in both investigated cases.
Towards the Application of Image Based Monitoring of USACE Large Civil Infrastructure
The extensive infrastructure portfolio maintained by the US Army Corps of Engineers (USACE) is in a state of degrading performance, with some components presently being used well beyond their design life. As part of an effort to maximize the impact of available maintenance funds, and ultimately extend the useful life of the infrastructure, the Engineer Research and Development Center (ERDC) is developing and deploying novel structural health monitoring (SHM) tools and methodologies. Currently deployed systems are based on data collected from applied, discrete, sensors, such as strain gages. However, with the increase in quality and decreased cost of modern camera systems, and the continued maturation of image based damage detection methodologies, it is clear that there are opportunities to incorporate image-based sensing technology into the USACE SHM strategy. The purpose of this paper is to provide an overview of the current state of the art in the use of imaging for inspections of large civil infrastructure, and to propose a new applications based on the needs of the USACE. The non-contact approach would provide a means to inspect large infrastructure in a safer, more timely, and more cost-effective manner.
Thermal Behavior of a Structure Characterized Through Three- Dimensional Temperature Signatures for a Temperature Driven Method of Structural Health Monitoring
Structural Health Monitoring (SHM) seeks to evaluate the condition of a structure by instrumenting the structure with sensors and then using a combination of structural and data analysis. One unifying factor across all structures is the effect of temperature changes on the behavior of the structure. Temperature Driven - Structural Health Monitoring (TD-SHM) considers temperature as the driving force in structural behavior, and consequently the main input in SHM. Temperature as an input is related to strain and displacement as output to form three-dimensional temperature signatures for the structures. These signatures are descriptive of the thermal behavior, as well as sensitive to changes in structural condition. The two main benefits of TDSHM are the applicability to any structure that undergoes daily or seasonal temperature changes, and the possibility to measure temperature as an input for a complete input-output model. This work examines one type of three-dimensional signature: the relationship between changes in temperature with changes in strain and rotation or tilt on a beam-like concrete structure. The three-dimensional figure is fit with a surface to quantify the behavior, and changes in either the goodness of fit or in the fit itself are indicative of changes in structural behavior. These methods are validated using data from a real structure; Streicker Bridge on campus at Princeton University.
Knowledge Transfer Between Buildings for Post-Earthquake Damage Diagnosis Without Historical Data
Automated structural damage diagnosis is important for improving the efficiency of disaster responses and rehabilitation. In conventional data-driven frameworks using machine learning or statistical models, structural damage diagnosis models are often constructed in a manner of supervised learning. The supervised learning utilizes collected historical structural sensing data and respective damage states (i.e., labels) for each building to learn the building-specific mapping between them. However, in post-earthquake scenarios, historical data with labels are often not available for many buildings in the affected area, which makes it difficult to construct a damage diagnosis model. Directly using the historical data from other buildings to construct a damage diagnosis model for the target building would lead to inaccurate results. This is because data-driven models assume that the data distribution of training dataset (from other buildings) is consistent with that of test dataset (from the target building), while in practice each building has unique physical properties and thus unique data distribution. To this end, we introduce a new modeling framework to transfer the knowledge learned from other buildings to diagnose structural damage states in the target building. This framework is based on an adversarial domain adaptation approach that extracts domain-invariant feature representations of data from different buildings. The feature extraction function is trained in an adversarial way, which ensures the extracted feature distributions are robust to changes in structures while being predictive of the damage states. With the extracted domain-invariant feature representations, the data distributions become consistent across different buildings. We finally adopt the damage diagnosis model learned from other buildings datasets to infer the damage state of the target building. We evaluate our proposed framework using data collected from 5 different buildings subjected to 40 different earthquakes. We transfer the model learned from multiple buildings to diagnose a new building subjected to new earthquakes. The results show an up to 33.8% improvement in damage detection accuracy compared to conventional methods.
Enhancing the Efficiency of Structural Condition Assessment by Integrating Building Information Modeling (BIM) Into Vibration- Based Damage Identification (VBDI)
The essentiality of Vibration-Based Damage Identification (VBDI) in monitoring the health of civil engineering structures has been highly emphasized by a considerable amount of studies. The more precise a VBDI method would become, the more efficient the data management system would be essential to be devoted to it. However, due to neglecting the essential constitution of data management system, collecting accurate and reliable condition assessment information would be always a challenge, moreover; loss of data, misinterpretation or even corruption might be occurred. In this study in order to enhance the efficiency of structural condition assessment derived by VBDI process, Building Information Modeling (BIM) has been utilized to demonstrate the full potential of structural condition visualization thorough a graphical BIM user interface developed in Autodesk Revit software. According to the authors' knowledge, no comprehensive work was dedicated to vibration-based structural health monitoring through automated graphical BIM user interface. However, a lot of studies have been carried out on BIM integration and strain-based structural monitoring. In order to investigate the viability of the present study, a three story steel scaled structure adopted and studied at both pristine and damaged states. Initially, the captured sensor data was utilized in experimental modal analysis (System Identification) to obtain modal properties of the structure. After, applying a vibration-based damage detection technique to identify and localize the probable damages in the structure, Dynamo Studio, a visual programming tool, would be the link conveyor of the derived structural properties and Autodesk Revit to visualize the current conditions of the structural elements based on a predefined color-coding scheme. In conclusion, it can be expected that, utilizing BIM as a visual data management system could be essential to alert engineers and decision makers, the probable damage locations from the changes of vibration characteristics in civil engineering structures.
Monitoring Concrete Strength Using Strain Energy Based Structural Health Monitoring Technique: Hypothetical Case Study of a Gravity Dam
Identifying the variation in Modulus of Elasticity (MoE) of concrete is essential to comprehend the state of functionality of the concrete structural system. At different stages of service life, from construction to the demolition or restoration, the structure needs to be monitored so that any anomaly in the strength of the structure, whether due to strength gain, inappropriate construction or due to damage, can be identified readily. A very small deviation in the strength may need an appropriate and robust identification parameter to detect the anomaly. The current research article considers four modal parameters namely Eigenfrequency, Displacement Mode Shape (DMS), Curvature Mode Shape (CMS) and Strain Energy Mode Shape (SEMS) in order to comprehend the strength reduction. Vibration-based health monitoring technique is employed to the 2D numerical model of a concrete gravity dam known as Koyna dam, as a hypothetical case study. The model is validated through the two-step process using available data and random excitation. In this process, the simulated acceleration response of the structure is analyzed to extract modal information. Upon validation, the model is used to study the strength variation during its strength gaining phase and initial service life. Results show that although Eigen frequency, DMS and CMS can be sensitive to the large strength incongruity, none of them is suitable for identifying and representing the minute change in MoE, especially during the strength gaining phase of concrete. While on the other hand, SEMS is found to be sensitive enough to be able to differentiate between the very small variation in the governing strength parameter. Therefore, it is concluded that SEMS can represent the modal response of a concrete structure quite precisely at different times of its service life and can differentiate between the variation of Eigenfrequency due to a large amount of damage at a small location and a small strength variation in the overall structure. This property indicates that SEMS is the most appropriate parameter for identifying the system of a mass concrete structure like a gravity dam.
Automated Generation of FE Model for Digital Twin of Concrete Structures from Segmented 3D Point Cloud
To achieve real-time structural health monitoring (SHM), a concept of digital twin – a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures.
Laser Ultrasonic Imaging of Built Facilities for Improved Lifecycle Management
Australia’s ageing infrastructure is in great need of a reliable monitoring technique. However, large dimensions, heterogeneous materials and complex shapes are challenges that must be circumvented. Ultrasonic waves are able to map incipient structural damages but they attenuate and maintaining reliability of the signals over the relatively large structural distances is a challenge. Thus, a high power ultrasonic technology would be useful for structural health monitoring. Recent developments in laser ultrasonic techniques have opened up the opportunity for remote inspection of large structures on account of its non-contact nature and its ability deliver a higher magnitude of energy (~1 Joule) in comparison to the existing methods (order of μJoule). Moreover, contact ultrasonic techniques, such as PZT based systems, rely greatly on the contact pressure between the PZT transducer and the surface under inspection. Laser ultrasonics can be totally non-contact and thus it alleviates the uncertainty of contact. Further their application can be extended to corrosive, hostile, geometrically larger and difficult to reach locations. In this paper, a laser based non- contact ultrasonic system is presented. Ultrasonic guided waves in the specimen are generated by means of Nd-YAG Pulsed Laser and the waveforms from these techniques are detected using non-contact Laser Doppler Vibrometer (LDV). The efficacy of laser systems has been illustrated with both amplitude and frequency response plots for a corroding steel bar.
Crack Detection Using Embedded Fiber-Optic Sensors in Reinforced Concrete Beams
Reinforced concrete structural components such as beams are subject during their service life to different loading conditions that may affect their durability and efficiency. This can reduce the safety level of the structure over time until it degrades completely and reaches its ultimate limit state. In particular, cracking conditions due to tensile conditions can be developed leading to the exposure of the steel reinforcements to the aggression of external agents, such as chlorides. Being able to monitor the development and the evolution of cracking is crucial and this research is aimed at this purpose. In particular, laboratory tests have been performed on reinforced concrete beams equipped with distributed fiber optics sensors to monitor the state of cracking. This technology has been selected because its inherent accuracy and low noise. Furthermore, since technology relies of optical signals, the sensors are not affected by electrical and magnetic noise. They are also resistant to high temperatures and chemically reactive environments. The main objectives of the presented first part of this research are focused on the deformation and temperature assessment after concrete pouring, crack detection, localization and opening quantification.
Remarks on the Collapse of the Polcevera Viaduct
In recent years several studies have been developed on structural collapses of buildings, leaving the bridges’ field partially investigated by few researchers. However, recent events of bridge collapses, namely in Genoa (Italy) on August 14th 2018 and in Kolkata (India) on September 4th 2018, have focused the public interest on the infrastructures’ safety for their consequences in terms of fatalities and injuries, but also of economy and social losses. The present work is focused on the disproportionate collapse of the Morandi’s Bridge in Genoa and consists in an independent study that is based on the available data from literature. The possible triggering events have been deepened starting from the corrosion and the fatigue issue of the steel tendons within the stays. Furthermore, the evolution of the loading conditions in terms of trucks’ number and individual truck weight from the bridge opening (1967) is considered. Therefore, (i) corrosion and fatigue models have been selected from literature and identified through original local weather parameters as temperature, humidity, chlorides, etc. while (ii) statistical historical data of the highway traffic in Italy have been collected. They are useful in the numerical analyses with finite element models of the Morandi’s bridge to identify possible collapse initiations. Some remarks on the role of redundancy in bridges structural scheme as the strategic characteristic to avoid the disproportionate collapse and to improve robustness are provided. Furthermore, the degree of staticindeterminacy of the structure with respect to disproportionate collapse is also discussed.
Dynamic Characterization and Vulnerability Assessment of a School Building in Italy
The accuracy of structural models significantly depends on the availability of input design parameters such as the dimensions of resisting elements, material properties, joint connection details, loading conditions, etc. When dealing with old structures, a lot of this information is not accessible, which leads to the introduction of many uncertainties into the model. In the last few decades, several structural health monitoring (SHM) techniques have been used to compensate such lack of data and perform more reliable structural analyses. However, SHM might result in costly and time-consuming surveys that can be as well affected by uncertainties due to environmental effects, measurement noise, and signal processing errors. This paper discusses and introduces a straightforward SHM procedure to collect the fundamental information to achieve a better accuracy and reliability of the finite element model of the considered building. The methodology starts with the acquisition of structural data from available construction drawings and Building Information Modeling (BIM) to create a preliminary finite element model. Then a series of non-destructive tests are performed using thermal camera, sclerometer, pachometer to identify the fundamental structural parameters. In addition, environmental noise measured through accelerometers is used to determine the principal frequencies of the structure. Harmonic excitations are then horizontally applied to resisting elements using a vibrodyne. The proposed methodology was tested on an RC school building located in Melzo (Milan, Italy). Statistical and signal processing techniques are then utilized to interpret the acquired signals and consequently update and calibrate the structural model for the vulnerability assessment. The processed signals revealed substantial differences with the original FE model. Thus, rigidity, mass and damping were modified accordingly.
Application of a Structural Health Monitoring System to the Minerão Stadium in Brazil
The paper describes the application of a Structural Health Monitoring (SHM) system to a reinforced concrete and steel Minerão stadium , the biggest stadium of the Minas Gerais State in Brazil. The structure is composed of a ring of post-tensioned concrete L-shaped frames. The roof of the stadium was recently widened with a steel frame structure connected to concrete beams. The SHM system is composed by a set of accelerometers and strain gauges, installed in 3 sections of the structure of the stadium and in 1 area of the parking lot. The purpose is to follow the accelerations, deformations and stress states caused to the structure by cyclic actions such as the movement of the crowd and the wind action. The position and the design of sensors were defined starting from a preliminary technical inspection of the structure made through the use of climbers, that showed the presence of damage and defects due to fatigue actions (loose of bolts in correspondence to connections) and water infiltrations, mostly in the connection joints between steel frames and the concrete deck. Due to the dimension of the structural elements, the SHM was set to transmit using the LAN internal network of the stadium. Also, the wireless solution was taken into consideration, but it has been shown not to be reliable enough due to loss of data clusters during transmission. Finally, a web-based monitoring software was designed, with functions of frequency field analysis, damage detection analysis and an early warning alarm for extraordinary events. The software is reachable everywhere with an on-line visualization of the structural health conditions of the structure.
Real-Time Monitoring for Condition Assessment and Long-term Behaviour of Metro Lines
This paper discusses two different series of results of a continuous monitoring system placed on the metro line in Vienna, Austria. The first one is represented by real-time results gathered over a time span of three years aimed at assessing the long-term behavior of massive train wheels. The second one is represented by results of impulse-tests performed in all major stations of the aforementioned metro line aimed at assessing the condition of the operating track systems as well as at selecting the best system installed. The conventional resilient wheels currently used on Viennese metro trains are planned to be substituted with massive wheels to increase performance, reduce costs and increase safety in regard to noise and vibration. Since the beginning of the project, specific trains were equipped with massive wheels and let circulate. Nine measuring systems were installed within the metro network in the form of monitoring stations to continuously record data. Radio-frequency identification (RFID) tags installed on the trains enable the identification of the aforementioned special trains when crossing the monitoring station. Selected indicators are permanently measured, statistically post-processed and transmitted in real time to our headquarter. On the basis of approximately 2,000 daily recorded trains, statistical signal processing has been performed thanks to this integrated structural health monitoring system, thus providing information on train condition and on the impact of massive wheels. In parallel to this type of investigation, impulse tests under specific conditions were performed in all the nine train stations with the scope of assessing the conditions of track systems, of comparing them and of discriminating among them in order to indicate the highest performance based on real-time data for the installed track systems. The integrated continuous monitoring system as well as the analysis of the big data collected well fit into the context of real-time diagnostics and remote sensing of infrastructures.
Structural Health Monitoring for the Assessment of the Dynamic Response of Existing Masonry Structures in Seismic Areas
Structural health monitoring is becoming increasingly popular in the field of construction management and maintenance of civil structures, especially those of strategic importance or relevant from a cultural heritage point of view. In Italy, through the national network of the Seismic Observatory of Structures (OSS), the Department of Civil Protection (DPC) monitors the oscillations caused by earthquakes in 160 constructions of public property. The paper reports the research activities performed on three monitored structures of the OSS, affected by the 2016 Central Italy earthquake swarm (max Mw=6,5). The three buildings (i.e. a school in Visso - MC, a former law court in Fabriano – AN and the city hall of Pizzoli – AQ) were constructed between 1920 and 1940 and are made of unreinforced masonry, with a good regularity in plan and elevation. Even though they present similar constructive features, the observed seismic-induced damages were largely different: heavy damages, near collapse, for Visso school; slightly damages for Pizzoli Municipality; and almost no damages for the building in Fabriano. The dynamic behavior of the monitored structures was studied through modal analysis techniques both under ambient vibrations and during the earthquake swarm. Data recorded by the monitoring systems are used to assess modal parameters variation and in particular to study the phenomenon of frequency and damping wandering during strong motion. Some severity indexes and dynamic parameters are calculated as well, including peak accelerations, structural amplifications, interstory drifts and elastic spectra. The results of modal identification will be used to assess the reliability of numerical models and their possible development to improve the predictive capacity in estimating the level of safety of existing structures.
Health Monitoring of Chloride Attack in Concrete Bridges
Development of a health monitoring system in situ is so important as to properly maintain concrete structures under severe salt attack. Research Association for Infrastructure Monitoring System (RAIMS) is engaged in research on health monitoring of bridges and thus experiments on concrete bridges under the severe environment are widely conducted. As a preliminary research on an applicability of the health monitoring system, sensing devices for early detection of chloride penetration and rebar corrosion have been applied to a prestressed concrete (PC) bridge in service. Three sensing devices of a corrosion sensor, a reference electrode and an embedded sensor are placed in the bridge. So far, monitoring of the corrosion degree due to chloride attack has been continuously conducted for more than two years. Further, a simple device for easy-installation in a local bridge is proposed.
Identification of Train Wheel Flat Accounting for Measurement and Modeling Uncertainties
In railway systems, wear-induced wheel flats (out-of-roundness wheel shape) are among the most common local surface defects. They reduce the train ride comfort, increase fatigue risk and raise safety concerns. A good knowledge of wheel flats (presence and position) can help decision makers avoid expensive operating interventions and unnecessary replacement of wheels. Although a significant amount of research has focused on wheel flat detection with the help of various monitoring systems, the quantification of wheel flats without interrupting railway operations is still challenging. Uncertainties are non-negligible in this context. Unfortunately, in most existing wheel-flat-detection methods, uncertainties are rarely taken into account. In this paper, a model-falsification framework is presented to integrate information obtained from measurements with simulation results. Uncertainties associated with both the model and measurements are combined and used to define the criteria to identify flat sizes. The proposed approach has been applied to a field test in Singapore. In the test, rail-pad-force measurement is obtained while a train with a wheel flat is running through the test track. The ranges of values for flat size identified using the proposed methodology include the true observation. It is also shown that the quantification of uncertainties is essential as the predictions that neglect them lead to an underestimation of the flat size.
Bone Fracture Sensing and Acoustic Data Communication
Approximately 12-15 million fractures occurred from 1998 to 2010 only in USA costing an estimated $23.4 billion on the healthcare system. Accurately monitoring the progression of fracture healing is essential to be able to advise patients when it is safe to return to normal activity. The most common method used to diagnose fractures and monitor healing is by radiographic imaging and evaluation of patient’s function. This paper presents the proof-of-concept of an electromechanical impedance (EMI) spectroscopy-based “fracture-sensingâ€ system fixed to a standard orthopedic screw and equipped with acoustic data communication that transmits fracture measurements to an external acoustic receiver. The concept is tested in an ex vivo pig limb at room temperature with the piezoelectric transducer fixed to a standard orthopedic screw and the acoustic receiver located externally on the skin surface. EMI measurements are recorded and processed in the frequency range from 40kHz to 500kHz. Acoustic data communication using on-off keying in time-domain is studied for distinct frequencies from 40kHz to 300kHz. The results demonstrate simultaneous bone structural sensing and acoustic data communication by means of the same sensor technology.
Research on Welded Joints Stress Condition Assessment of Railway Freight Car Welded Frame with Considering Stress Concentration Effect
In recent years, fatigue problems of bogie welded frame have occur at welded joints with stress concentration, which reflects the current design method of welded joints needs to be improved. The structural stress method can effectively identify the stress concentration of welded joints. In this paper, the structural stress method in ASME standard is programmed, and combined with BS EN15085-3:2007 standard, the assessment route of stress condition of welded joints of freight car welding frame is put forward. Based on the fatigue load of EN13749:2011, the Master S-N curve with 98% reliability -2σ is selected to predict the fatigue life of the weld, and the stress factor is calculated according to the equivalent structural stress of the weld. The analysis shows that the stress factor of the weld of the frame cross beam and brake hanger, cross beam and traction rod base is larger, the maximum value is 0.81, the stress state grade is "medium", and the stress condition of the other welds is "low". The stress condition assessment method proposed in this paper can identify the stress concentration of welded joints. In order to improve the rationality of design, it is necessary to study the stress condition of welded joints in the design stage for other complex structures.
Strategy for In-Situ Determination of Autogenous Self Healing Capacity Through Limited Measurements
A new strategy is presented in this paper for in-situ measurement of the healing effect without pre-knowledge of the environmental condition. Exploiting the fact that the electrical conductivity in the crack is lower than that of undamaged section, the effective macroscopic cracking effect is first distinguished by frequency dependent impedance measurements both along (parallel to) and across the cracks. Dielectric spectroscopy along the cracks is found to correlate well with gravimetric measurements and can therefore be used for inverse approximation of moisture content. The effect of moisture change on the equivalent resistance in the direction across the cracks can then be derived approximately from measurements under various moisture contents. The approximated resistance vs moisture curve is found to reflect the evolution of autogenous self-healing, and a curve indicating almost complete healing agrees well with the recovery in member stiffness measured from loading test.
Real-Time Remote Monitoring of Power Plants in Terms of IIoT and Cloud Computing
Structural health monitoring measurements in power plants were for many years accessible only on the power plant site. Monitoring of relevant signals and operating variables outside the power plant was impossible and concurrently, the structural health monitoring or condition monitoring was based only on measurement evaluation on site. The breakthrough brought the Industry 4.0 in 2010s. The industrial internet of things connects units over the internet giving the power generation segment new opportunities like remote monitoring of the processes and operating variables. This paper describes the developed remote monitoring system – RMS – including hundreds of operating variables in power plants and covering monitoring function of power plant components in real time. The system is based on data collection unit installed on the power plant site mainly featuring as database storage. TCP/IP communication over the internet connects these units with the cloud computing server. Cloud computing and user-friendly visualization makes this system easy to use for the end-user practically all over the world. However challenges like cyber-security, reliability, long-term stability had to be considered in such a solution. Primary, the system is used to monitor operating variables from turbine control system. Temperatures and pressures in a turbine and other power plant components give the user/operator a basic preview of the system function as a whole. Secondary, the system is used to interconnect any other advanced diagnostic devices such as measurement of the blade vibration, monitoring of rotor/stator rub etc. Monitoring of blade dynamics is necessary for blade state evaluation. Symptoms of change in blade dynamics monitored by RMS are also particularly described in this paper. Evaluation of structural health of the blade material is based on Fast Fourier Transform of the specific monitored variables. Measured data are easily accessible through the web and also by mobile device application which was developed as a simple thin client for visualization of the main power plant functions. Each authorized user of RMS can access the real-time data over the internet.
Research on Wheel Fatigue Test of Railway Vehicles Based on Non-contact Full-field Strain Measurement Technology
Wheel is one of the most important component in the running part of railway vehicle, and its fatigue performance directly affects the running quality and safety of the vehicle. When carrying out wheel fatigue test, the maximum radial test stress amplitude applied on wheel spoke is 240Mpa according to EN13262. The wheel-axle model is meshed by Hypermesh, and then finite element analysis is carried out by Ansys to obtain the required test load and the maximum radial stress position. The required load is input into the wheel-axle fatigue test system, and speckle is sprayed at the maximum radial stress area of the spoke plate. The actual stress value measured by non-contact full-field strain measurement technology based on 3D-DIC (digital image correlation) have good repeatability with the stress value measured by strain gauges applied at the maximum radial stress of the spoke plate, and full-field stress-strain nephogram of the concerned region can be obtained. Compared with conventional measurement methods, non-contact full-field strain measurement technology has the advantages of high accuracy, fast response, wide measurement range, small temperature effect and low environmental requirements. The application of noncontact full-field strain measurement technology can realize the closed-loop control of the load in key area during the process of design, analysis and test of wheel, and improve the accuracy and efficiency of the test.
Impact Index Method for Railway Catenary Short-Wave Irregularity Evaluation
A new Catenary Impact Index (CII) for evaluating catenary short-wave diseases, such as the bad hanger ear, the wire with hard bend, the curved hanger and so on, is proposed by use of the pantograph vertical acceleration. In comparison to amplitude evaluation method, the catenary impact index method can reduce the influence of various random factors. Firstly, the filter frequency range for calculating the root mean square (RMS) of the pantograph vertical acceleration is obtained by deeply analyzing its frequency distribution characteristics. Then the RMS and CII of the pantograph vertical acceleration, instead of the original signal, is calculated and used for diagnose catenary short-wave diseases. This method demodulates the catenary short-wave impact from the high-frequency signal to a low-frequency signal, and has the advantage of being more stable. Furthermore, the appropriate window length is selected to calculate the energy index RMS according to impact response characteristics of the catenary short-wave disease. Finally, the pantograph acceleration data is normalized according to the average value of massive data to solve the problem that the absolute threshold is difficult to determine. This new method is applied to analyze the measured data from Catenary Inspection Vehicle, and the field verification results show that it is efficient and dependent in detecting catenary short-wave diseases.
Automated Fault Diagnosis with Calibrated Artefact Probing
Modern five-axis machining centres are able to manufacture parts with extreme pre- cision; they are, however, not free from error, and error affecting the machine kinematics in particular is observed to change with time. To maintain tolerance requirements, machine operators will routinely verify the kinematic health and take action where necessary. One common method for verifying rotary-axis health is calibrated artefact probing, the results of which can be analysed to identify specific kinematic fault states and inform maintenance actions. This analysis requires specialist knowledge, so in order to ensure fault diagnosis is accessible to all users, an automated inference system is desirable. A Residual Neural Network was trained with artificially-generated data, to identify seven common kinematic fault states. The classifiers performed favourably when applied to a test set acquired from operational machine tools, indicating that the approach is viable. However, some faults were found to be easier to identify than others, so further work on developing the training set is required before the method can be deployed in industry.
Damage Identification on the Leaf Spring of a Railway Freight Wagon—Findings from Snap-Back Experiments with Transient Excitation
Structural Health Monitoring (SHM) systems for structural parts of freight wagons are subject to special conditions. An important task of those systems is to detect damage to the leaf springs of the wagons. In such a case, the damaged wagons must be removed from the train and repaired at the next opportunity. However, the framework conditions for such SHM systems for wagon suspensions are a challenge in some other respects: on the one hand, the commercial framework is very tight which prohibits the use of expensive technology. The hardware required for each wheelset must not only guarantee the operation of the SHM algorithms, but also the following functions: data storage, communication with the other wheelsets and a control center somewhere on the rail network, data management and system control and power supply. All tasks have to be performed under extreme environmental conditions like rain, snow, ice, heat, shock loads, dust, etc. Because of these requirements slim and robust SHM algorithms have to be developed. Based on these requirements, the proposed paper deals with the damage detection at the suspension of a railway freight wagon and firstly reveal, that the measured accelerations changes with the damage in a characteristic way. The authors show that the damage of the suspension is accompanied by a reduction in spring energy in the measured signals and a shift of the higher vibration modes. In this context, the paper has no pretense to develop new methodologies for damage identification but the simple findings from the experimental tests can be useful for the design of SHM systems for railway vehicles. At the end, a simple SHM approach for a low number of sensors with practically positions at the wagon is proposed.
Fault Diagnosis of Rolling Element Bearings on Low-Cost and Scalable IIoT Platform
Across industries, rolling element bearings have been found to be among the largest contributors to machine downtime and the costliest to replace. Thus, a reduction in unexpected bearing failures will lead to an increase in machine availability, a reduction in maintenance and downtime costs, and an improvement in customer satisfaction. Over the past few decades, deep learning has been recognized as a useful tool for fault diagnosis of rolling element bearings. Although existing deep learning approaches can achieve satisfactory accuracy in bearing fault diagnosis, most of these approaches rely exclusively on data and do not incorporate physical knowledge into the learning and prediction processes. To address this challenge, this study develops a deep learningbased fault diagnosis approach by proposing a novel physics-based feature weighting (PFW) technique. The proposed PFW technique leverages the fault characteristic frequencies of a bearing to weigh the vibration features based on the amount of faultrelated information that they are expected to carry. The weight learning component of PFW comes from the ability to learn (or optimize) the parameters of the weighting functions while training a deep learning model. The performance of the developed approach with the proposed PFW technique is compared with that of traditional convolutional neural network.
Identification of Out-of-Round Wheels on Heavy-Haul Railway Lines by Using Ballast Pressure Data
A Model-Based Estimate Method (MBEM) was introduced to estimate wheel-rail force by using track dynamic response data, the estimated result then be used for of OOR wheels identification. The function of dynamic force transfer between the wheel-rail force and tie displacement was developed based on a double layers track model. A case study was conducted to verify the result. Test equipment was made for tie-ballast interface pressure measurement. The equipment was installed on a heavyhaul railway line in the United States, long-term monitoring on ballast pressure was conducted. A series of preprocessing methods were used to obtain the normalized pressure data. According to the comparison between Wheel Impact Load Detector (WILD) data and ballast pressure data, the relationship between ballast pressure and wheel load was established. The wheel-rail force of each wheel was calculated by using the MBEM method.
Internal Corrosion Detection in Pipes Using Piezoelectric Transducers
This paper introduces a new method for internal corrosion detection in metal pipelines using an externally surface bonded (SB) piezoelectric transducer working on electromechanical impedance technique (EMI). Specifically, changes in mechanical properties of the structure is monitored by observing the changes in structural mechanical impedance obtained through the bonded piezo electric sensor thereby employing EMI technique. Through controlled incremental corrosion induced in the pipe segment considered for experimentation using accelerated corrosion tests a realistic corrosion environment was proposed. The hourly exposure to corrosion is correlated with the signatures acquired through the sensors. Sensitive parameters such as temperature and humidity were kept constant to discern changes solely due to corrosion. Through timely phased acquisition of signatures using an LCR meter, shift in conductance may be monitored. Changes in structural parameters such as stiffness, mass and damping is calibrated through conductance signature analysis. Development of corrosive products was monitored simultaneously with scanning electron microscopy (SEM). Experimental results show that good correlation exists between the obtained SEM analysis and change in structural parameters. A steady decrease in stiffness and mass was witnessed with a noticeable trend in magnitude. In essence, a simple, easy implementable method in employing EMI method to detect internal corrosion in pipelines is demonstrated.
Analysis of Pathological Tremor Behavior: Application to Design a Passive Tremor Attenuator
A total of 144 measurements of the left-hand finger-posture is processed for 3 neurodegenerative disordered patients. The current work concentrates on analyzing the changes in the tremor behavior within the same patient for different hand postural positions repeated for 3 days. The measured datasets represent the 3-directional accelerometer’s signals in addition to the electromyographic (EMG) measurements of the wrist antagonist muscles. The study shows the effect of the muscles behavior on the shape of the tremor movement, the identification of the muscles signals damping ratios, and provides a statistical analysis of the frequency and amplitude of the dominant peak. The obtained measurements serve in providing information useful in the specification of the undesired frequencies for a patient and the design of a tremor suppression device.
On the Use of Distance Measures in the Phase Portrait for Damage Localisation and Severity Assessments
Damage localisation and severity assessment are two essential levels in the field of structural health monitoring (SHM). One of the challenges in SHM today is to extract damage-sensitive features from the measured data and correlate them with the location and severity of the damage. Common types of structural damage, such as cracks, or even joint failures due to loosened bolts, usually result from a slow-time degradation process and show strong nonlinear behaviours. In this case, traditional features may not be able to distinguish between the undamaged and damaged structure and track the degradation process effectively and efficiently. In this paper, the damage features are described by a distance measure in the phase portraits to locate the damage and quantify its severity. The phase portraits are reconstructed from the measured time series of various conditions. Instead of focusing on the warp of the trajectory or the phase space topology, this paper introduces a simpler way, using a distance metric based on Mahalanobis distance (MD) to demonstrate the features in various damage scenarios and the undamaged one. Combined with a data-driven pattern recognition method, like Artificial Neural Networks (ANNs) and Gaussian Processes (GPs), this distance metric in the phase space can detect, locate and assess the severity of the damage in a standard classification approach. A simple laboratory structure is set up to verify the proposed feature. A comparison between ANNs and GPs for the damage severity problem is also done.
Big Data in Experimental Structural Dynamics by Synchronous Ensembles of Collocated Acceleration Signals (CAS): The Reciprocity Principle Failure and Advanced Diagnostics for Composite Material Structures
Acquisition of engineering Big Data (statistically similar large ensembles of structural dynamics signals) accompanied by proper physics-mechanics interpretation could enhance considerably structural health monitoring of complicated structuralmechanical systems, their complex geometric features rendering quite difficult the physics-based interpretation of experimental free wave-vibration signals. Towards exploring the most natural and technologically feasible way of recording (data acquisition and sensors technologies) and interpreting Big Data (processing of arbitrary high resolution space-time samples of motions), we considered composite structures as prototypes of complex structures, the latter being prone to damage both during manufacturing and service cycles. Specifically, we acquired at several regions synchronous Collocated Acceleration Signals (CAS). Introduced then is the concept of intersections of synchronous CAS ensembles by a novel application of the Proper Orthogonal Decomposition (POD) transform. Regarding composite materials-based beams, we have found that three synchronous CAS ensembles intersect strongly partially since they share the same co-dominant POD vibration modes. In addition, partial weak intersection of three synchronous CAS ensembles is detected, indicating that the Reciprocity Principle (RP) fails gradually for all natural time scales. The RP holds strongly partially, macroscopically, when the intersection is sharp over a small number of POD modes. Clearly the failure of the RP forms a needed basis on which one could build analytic procedures on POD-based reduced dynamics, modal like vibration patterns, aimed at developing advanced diagnostics, system identification and damage detection for composite materials-based structures, among others.
Composite Structures Integrity Monitoring Using Rotating Electrical Signals
Composite Electrical impedance to evaluate composite damage is receiving growing attention from the research community as an alternative approach to ultrasounds and other non-destructive techniques. It is still in development, but recent years steady technologies advanced have increased understanding and process capability, in fact in literature there are even reports of damage detection using impedance missed by other technologies. Ultrasounds technology is very mature and robust and remain elite technique but when it comes to access recesses and complex geometries further advantages could be achieved if a smaller and relatively simpler monitoring equipment could be available. Objective of this poster is lay preliminary research to evaluate the possibility of simplifying impedance analyses using multiple circularly arranged sensors that can be interrogated with specific sequences and interpreting algorithms.
Structural Health Monitoring for Damage Localization and Imaging Using a Linear and Nonlinear Ultrasound Subharmonic and Modulation Method
Structural health monitoring (SHM) of components is fast becoming a vital park of the in-service maintenance and evaluation of components and structures throughout various industries. This work presents a linear and nonlinear ultrasound subharmonic and modulation method for the localizations and imaging of barley visible impact damage in composite materials. Both linear and nonlinear techniques are used in order to combine the high sensitivity of nonlinear techniques with the accuracy of linear methods in damage cases where large impedance mismatches occur. A sparse array of ultrasound transducers and sensors was used to evaluate the linear and nonlinear subharmoic and modulated elastic response of a complex composite stiffener panel using a nonlinear multi-path reciprocal (MEMR) methodology. The reciprocal method determines the location of damage between two paths, with higher nonlinear responses occurring at sensors close to the damage and vice versa for linear measurements. The reciprocal method is combine with a statical method to group individual frequencies which relate to the damaged region and creates a point cloud which locates damage according to coordinate positions (x, y) and images damage according to a linear or nonlinear amplitude (z). Traditionally, SHM methods only provide damage localisation information, the proposed method not only provides imaging of damage region but does so without priori knowledge of the wave velocity and without baseline measurements of undamaged samples.
Low-Cost Electromechanical Impedance Testing Damage Detection of Submerged Civil Structures
Electromechanical (E/M) impedance testing using piezoceramic (PZT) patches adhered to the surface of a substrate structure is regarded as a promising structural health monitoring (SHM) technique due to its ability to detect local and incipient damage in diverse types of structures. E/M impedance testing utilizes the direct/inverse piezoelectric effect, and the PZT patch acts as both sensor and actuator. Any damage done to the substrate structure will change its mechanical impedance, thus further changing the coupled electromechanical impedance of the patch-structure system, which can be measured using impedance analyzers. Research in this field so far has largely been laboratory-based in ideal environmental conditions with impedance measurements taken using bulky and expensive commercial impedance analyzers. Moreover, little has been done for structures submerged in water. This paper investigates the potential for using low-cost electromechanical impedance analyzers for damage detection in submerged civil structures. The goal is to close the critical gap between laboratory development of the E/M impedance testing method and its deployment in the field. A single-board computer (SBC), called Red Pitaya (RP), is used to make high-fidelity impedance measurements and wirelessly communicate with a PC. Damage detection tests are established on steel beams to test RP’s efficacy as a SHM tool. Damage detection tests on beams are conducted on submerged specimens and the results demonstrate the potential of using RP for detecting damage in submerged civil structures using low-cost impedance testing.
Damage Detection of Reinforced Concrete Bridge Considering Temperature Influence Based on AANN and FCM-PSO
In order to eliminate the temperature influence on modal frequencies and identify structural damage in RC bridge accurately, a two-step damage identification technique is proposed without measuring temperature data. Firstly, based on the modal frequencies of undamaged bridge under varying temperature, the nonlinear principal component analysis using auto-associative neural network (AANN) is performed to extract the underlying mapping relationship. The difference between modal frequencies of damaged bridge and values reconstructed by AANN is used as damage identification index. Secondly, fuzzy C-mean clustering (FCM) algorithm improved by particle swarm optimization (PSO) algorithm (FCM-PSO) is used to identify damage existence and degree. The difference of modal frequencies from training cases are classified into several clusters based on FCM-PSO analysis, and the cluster centers for damage grades are constructed. Damage degree can be identified by calculating the fuzzy memberships between identification indicator vector and cluster centers of damage grades. This proposed technique is capable of identifying damage without measuring temperature data. Damage detection results show that the damage extent can be accurately evaluated after the damage is introduced.
A New Online Structural Health Monitoring Technique to Detect Subtle Damage Considering Environmental Variability
Detecting damage at the earliest possible stage is the most desirable feature of any structural health monitoring scheme, for its successful practical implementation in the field. Particularly, for a small damage in its incipient stage, the minor changes in the dynamic characteristics of the structures, alter only some specific modal responses. Hence the damage features present in the modal response of only some limited modes due to the minor incipient damage will not get highlighted in the measured raw dynamic signatures. Also, the presence of environmental variability (EOV), which alter the dynamic characteristics and signature, mask the existence of the minor incipient damage from diagnosis, while using conventional damage diagnostic algorithms. In this paper, a new online health monitoring technique is proposed to handle the EOV and locate the minor damage in the structure. In the proposed online SHM scheme, we use the cointegration technique to handle the EOV. During online monitoring, the cointegrating vectors are obtained from the recent healthy data denoted as ‘baseline data’, collected from the structure. Subsequently, these vectors are used to filter out the confounding effects of EOV of the current data. During damage diagnosis with the current data, if the state of the structure is found to be healthy, then the ‘baseline data’ is updated with the current data along with their corresponding freshly evaluated cointegrating vectors. We later employ the blind source separation (BSS) technique on the cointegrated time series, free from EOV, to decompose the dynamic response into modal responses. Then we employ an automated algorithm proposed in this paper, on the decomposed signals (i.e. modal responses) in order to identify and isolate the modal responses with the damage features. Finally, the location of the damage is identified using a damage index, formulated with the isolated modal responses. Numerical simulation studies and experimental studies are carried out to test and evaluate the proposed online damage diagnostic technique and their capability in identifying minor/incipient damage like small cracks considering environmental variability with measurement noise.
Investigation of Broadband High-Frequency Stochastic Actuation for Active-Sensing SHM Under Varying Temperature
The main objective of this work is the investigation and numerical assessment of random (stochastic) broadband high-frequency actuation for active-sensing structural health monitoring (SHM) via stochastic time-series models, and the comparison with traditional deterministic tone-burst actuation under varying temperature. The main hypothesis examined in this work is that random broadband actuation, within the range of 100 KHz to 1MHz, may provide additional structural information due to the excitation of additional vibration modes compared to deterministic waves with the potential cost of requiring more elaborate signal processing, modeling, and diagnostic methods. Initially, a deterministic 5-peak tone burst signal is used to excite guided-wave propagation under varying temperature in an aluminum plate outfitted with piezoelectric disk transducers. Next, the same setup is used to induce broadband high-frequency random actuation in order to enable the stochastic modeling of the structure via AutoRegressive (AR) and Functionally Pooled (FP) models. The results of the study indicate the potential of using broadband high-frequency stochastic actuation for active-sensing SHM.
Structural Health Monitoring Systems Based on SH Wave Transducers Sparse Array and Phased Array
Guided wave based structural health monitoring (SHM) had been widely used in modern industries to detect early damage in structures and thus prevent catastrophic failure. In recent years, Lamb wave based SHM had been extensively investigated but the inherent multi-mode characteristics and dispersion limited its further development. In comparison, the fundamental shear horizontal (SH) wave based SHM seems to be more promising for its non-dispersion. However, the lack of omni-directional SH wave piezoelectric transducer (OSH-PT) always restricted its development. In this paper, we firstly proposed an OSH-PT consisting of twelve elements based on thickness-poled thickness-shear mode. Both simulations and experiments indicated that it can generate single mode of SH wave with signal-to-noise ratio (SNR) up to 26 dB and receive SH wave only with SNR up to 23 dB. Moreover, the deviation of its omni-directivity was only about 6% for both SH wave generation and reception. After validating its performance, four OSH-PTs was employed to construct a sparse array system for damage detecting in plates. Results showed that the proposed system can accurately locate a through-thickness hole in a super resolution of 0.12λ (4 mm) at varied frequencies with located error less than 12 mm. Besides, it can also detect multi defects based on baseline subtraction method. Next, the twelve-elements based OSH-PT was optimized by varying its size and element’s number. Results indicated that after size optimization, even for two half-rings based OSH-PT, it can still generate SH wave omni-directionally with SNR up to 20 dB. This half-ring based OSH-PT was very suitable for practical SHM applications for its simple structure and good performances. Finally, a phased array consisting of nine half-ring based OSH-PTs was developed for defect inspection and total focusing method (TFM) was employed for the experiments. Results showed that the phased array system can detect both surface defect and throughthickness defect effectively without baseline. It can detect a through-thickness hole as small as 2 mm in diameter with located error only about 6.3 mm. Moreover, it can also detect multi defects simultaneously. Considering the easy fabrication of the OSH-PT and the low working frequency thus large monitoring area of the proposed systems, this work is expected to pave the way to SH wave based SHM.
Stress Monitoring Based on Lamb Waves Measurements and a Convolutional Neural Network
Monitoring of static and dynamic stresses is crucial for structural performance evaluation and prevention of unexpected structural failure due to overloading, stress concentration and fatigue. For quasi-static stress monitoring, ultrasonic measurement is an attractive method because of its non-destructive nature and readiness for online monitoring. The well-established acoustoelastic effect shows that the ultrasonic wave velocity exhibits a linear relationship with the stress within the linear elasticity region of a material when the wave propagates through the material. In this study, an online stress monitoring technique is proposed for metallic plate-like structures based on Lamb-wave measurements and a Convolution Neural Network (CNN) under dynamic as well as static loadings. First, an aluminum (6061-T6) plate specimen is fabricated, and piezoelectric (PZT) transducers are installed on the specimen. Then, different levels of static loadings are applied to the specimen, and the ultrasonic responses are obtained at each loading level. For Lamb wave generation and sensing, a conventional data acquisition system is used, and the applied loads (ground truths) are measured using the load cell built in the loading machine. Next, a CNN is designed and trained by defining the ultrasonic response in the time domain as the input and the measured stress level as the output. For the input, one-dimensional time domain ultrasonic responses are rearranged to two-dimensional images, and the images constitute the inputs of the CNN without feature extraction. Finally, the performance of the trained CNN is examined using blind test data obtained from various static and constant amplitude cyclic loading conditions. For the monitoring of dynamic loadings, the data acquisition parameters such as the signal duration and the repetition rate are controlled and optimized. The uniqueness of this technique lies in (1) automated stress estimation without conventional feature extraction based on human intervention such as acoustoelastic effects, and (2) stress monitoring under constant amplitude cyclic loading loadings up to 5 Hz.
Reliable Damage Monitoring Under Time-Varying Conditions Based on Gaussian Mixture Model and Delay-and-sum Imaging
As an effective structural health monitoring (SHM) technology, piezoelectric sensor network and guided wave (GW) based damage imaging method has been widely researched. However, aircraft structures are usually subject to random and nonlinear time-varying conditions, making it hard to achieve reliable damage imaging and localization result. Aiming at this issue, this paper proposes a Gaussian mixture model (GMM) and delay-and-sum imaging based reliable damage monitoring method. In this method, the GMM is used to suppress the uncertainties on GW signals caused by timevarying conditions and to construct the time-invariant feature signal which is able to reliably describe the damage induced signal change. After obtaining the time-invariant feature signals of every GW pitch-catch path in the adopted sensor network, the delayand- sum imaging can be performed to realize reliable and accurate damage imaging and localization. In order to verify the feasibility of the proposed method, experiments are conducted on a stiffened carbon fiber composite plate within a temperature range from -20℃ to 60℃. The experimental results show that the method is able to suppress the time-varying influence and locate damage accurately under temperature variation.
SETRIC—Sparsity Enforcing Time Reversal Imaging Concept for Ultrasonic Guided Waves
Ultrasonic guided waves are used for the detection, localization and severity estimation of damage in technical structures. In the past few years multiple damage localization techniques were developed in the framework of structural health monitoring (SHM) that use Lamb waves to inspect thin structures. In this work a novel imaging concept for damage localization is proposed and compared with existing image reconstruction methods based on simulated signals. The so-called Sparsity Enforcing Time Reversal Imaging Concept (SETRIC) represents a combination of time reversal (TR) and model-based compressed sensing (CS). In TR the received signals, containing the scattering response from damage, is time reversed and send back into the structure. The energy accumulates at the position of the damage because of the reciprocity of the wave equation. On the other hand, CS models the ultrasound scattering for each pixel, stores the signals in a dictionary and then approximates the measured signal by solving a linear equation based on a sparsity assumption, e.g. orthogonal matching pursuit (OMP). In SETRIC, we use TR to extract the dominant features from the signals of the damaged structure, obtained from the right singular vectors of the transfer matrix, and then use CS to generate the image.
Nonlinear Guided Waves for Fatigue Crack Detection in a Steel Joint
Higher capacity and faster rail transport systems are in demand due to population and economic growth which imposes a greater possibility of fatigue failure in railways. This paper presents an experimental study of nonlinear guided wave-based fatigue crack identification in a steel joint subjected to tensile fatigue loadings. The steel joint is considered as a section of a railway bogie, which is a vital component of the train holding the axles, suspension and brakes, and is located at a stress concentration area of the structure. The second symmetric mode (S1) of Lamb waves was selected as the input signal and excited by piezoelectric transducers which were installed on the top surface of the joint. The sensing paths were aligned perpendicularly to and at various distances away from the crack. When the waves interacted with the crack, higher harmonics were produced owing to contact acoustic nonlinearity (CAN). A nonlinear index based on the second harmonic characteristics is exploited to define the nonlinearity. The finding from this experiment indicated that there was a significant change in nonlinearity at the stage of crack initiation and crack growth. Therefore, nonlinear guided waves offer the feasibility in detecting fatigue crack in steel joint.
Fatigue Crack Identification in Weld Zone of Bogie Using Guided Wave
As one of the most convenient means of urban and intercity public transportation, rail train has been developed rapidly in recent years. The bogie is an important structure of rail train, transmitting the traction, braking force, lateral force and vertical force during the traction operation. With the development of high speed and heavy load, the appearance of fatigue cracks in rail train bogies is increasing, which reduces the driving life of rail train and even causes serious traffic accidents. Real-time monitoring on the integrity of bogie is an inevitable requirement for ensuring the safe operation of rail train. Guided wave based active structural health monitoring was developed to identify the fatigue crack of the bogie frame in this paper. Experiments were conducted on a welded T shape specimen with thickness of 12mm. 10 PZTs were mounted around the weld zone of specimen, five of them were used as actuators, and the other five PZTs were used as sensors. Five-peak modulation narrow-band sine waves were used as the exciting signals. From the sensor signals, the advanced Damage Index (DI) was employed to identify the propagation of crack. The experimental results demonstrate that the size of crack damage as small as 2mm around the weld on the bogie frame can be successfully detected.
Advanced Non-destructive Technique for Debonding Inspection of Aircraft Composite Structures
A preliminary Structural Health Monitoring system for the detection and measurement of the debonding between the skin and the stringer flange, induced by low energy impacts in composite aircraft wing panels, is proposed. The whole system is conceived like an off-line system, aiming to be an alternative method to the current NDI/C-scan techniques, leading toward the reductions of costs and times. The system is composed of high frequency sensors/actuators piezoceramics (PZT), while the control algorithm is based on the elliptical triangulation method, in particular on the guided waves propagation inside the structure. This method is based on the measurement of the perturbations induced by discontinuities present within the structure, like the debonding. Such discontinuities, if crossed by a wave, affect the signal in both amplitude and path. Numerical results on small-scale stiffened panels made of carbon fiber reinforced plastic material are proposed in the work. Preliminary experimental results are presented and the complete verification and validation of the approach at laboratory level is in progress. Besides, the work is propaedeutic to bonded composite parts in general: parts situated in positions difficult to access or in remote locations with substantial time-consuming inspection operations and bonded patches are often used as economical repair strategy in place of component replacement, with significant cost-savings. In addition, due to the uncertainty of long-term adhesive performance and the inability to continuously assess the repair condition, this technique could overcome the current design practices for bonded parts that are inherently highly conservative; in fact, additional “special factorsâ€ to the design limit loads are applied to the structural parts and even more in absence of the repair.
Guided Wave Propagation Based Analysis of Non-linear Debonding Effects in a Composite Structure
Carbon-fibre reinforced composite structures are extensively used as a specialized lightweight construction material in the aerospace, aeronautics, automobile, construction, marine and wind-turbine industries, due to their high energy-absorption potential, construction flexibilities and high strength-to-weight ratios. In several specialized applications and construction requirements, stiffeners with variable shapes (e.g. T-section, L- section, I- section) are used in such composite structure. Such stiffened composite structures are often prone to the various linear and non-linear type of damage (such as- debonding, breathing crack, kissing-disbonds) due to ageing, cyclic loading, fatigue, impact, improper handling, and environmental impacts (temperaturefluctuations, moisture, turbulence). The ultrasonic guided wave (such as- Lamb wave) propagation based inspection strategies have the potential to effectively detect defects and/or structural changes in composites/metallic structures. The guided wave based inspection techniques have long-distance propagation capacity, penetration capability into several hidden layers, and in these techniques, effective identification of different wave modes plays a vital role and the wave propagation is dependent on the structural material properties, dimensions, the frequency of excitation, loading and operating conditions. Therefore, it is important to study the influences of those hidden damages on the propagating guided wave signals, in order to effectively identify and characterize them for avoiding anomalies during the structural health monitoring of such real-life stiffened composite structures. In the study, a guided wave propagation based non-linear debonding response analysis is experimentally and numerically carried out for a stiffened composite panel. Towards this, a series of finite element method based numerical simulations are carried out in ABAQUS for a stiffened composite structure in presence of a plate-stiffener debonding region and the obtained simulation results are verified by conducting laboratory experiments. Significant influences on the propagating guided wave signals are observed due to the presence of non-linearity at the debonding region in terms of the generation of higher harmonics. Based on the identified differential changes in selected higher-harmonics magnitudes it is possible to effectively identify debonding regions.
Alternative Excitation and Data Analysis Techniques for Damage Detection in Metallic Plates
Strategies for reliable damage detection of thin-walled structural components (e.g. metallic shell structures or stiffened composite panels) are required in various industrial branches, e.g. aerospace, shipbuilding, tower construction, etc. In the last years, structural health monitoring (SHM) methods and systems, based on guided ultrasonic waves and especially Lamb waves, have been successfully investigated. On the other hand, vibration-based SHM approaches showed their damage detection capabilities in many application domains, including for instance the surveillance of wind turbine structures [1] and civil infrastructure. Given by the stochastic nature of the ambient excitation of such structures, a variety of statistical signal processing techniques have been developed, including for example Null Space based Fault Detection (NSFD) method [2] or methods based on the changes of measured signals energy (e.g. Root Mean Square (RMS) analysis). The main goal of the present paper is to study statistical signal processing techniques, originally developed in the context of vibration-based SHM, for guided wave-based damage detection. Therefore, an experiment has been developed consisting of a metallic plate and two surface bonded piezoelectric transducers. Deterministic and also stochastic excitation was used to identify damage at different spatial locations. Experimental findings will be presented and discussed in order to derive the respective strengths and weaknesses.
Ringing Suppression for Ultrasonic Nondestructive Testing using Iterative Learning Control and H-infinity Synthesis
In this work, the use of iterative learning control (ILC) in ringing suppression is assessed with the intention of generating precise ultrasonic wave pulses for nondestructive testing (NDT) purposes. The premise of ILC is that the performance of a system, which is repetitively executing the same task, can be improved by learning from previous attempts. In order to develop a systematic approach to the design of the L- and Q-filters for the ILC, a previously proposed Hâˆž-synthesis method is employed. To this end, an experimental frequency analysis is conducted for commonly used ultrasound transducers, and a low-order model is fitted to the resulting data near the transducer’s peak frequency. Based on this model, appropriate L- and Q-filters are synthesized and the ILC is trained to generate the desired wave packet. The simulation results indicate convergence of the proposed ILC algorithm, and thus generate the necessary driving trajectory. By taking the generated signal from simulation and using it as the driving signal in laboratory experiments, it is shown that ringing effects, i.e. distortions of the induced wave packets, are significantly reduced. The impact of such an improvement in the context of guided ultrasonic NDT is demonstrated through a time-of-flight experiment for edge detection, showing promising results and a positive starting point for future work.
An Integrated Ultrasonic Imaging Technique using Laser Scanning System
This paper presents an integrated methodology to improve the performance of laserscanning based damage detection for large structures with various obstacles. Two different ultrasonic imaging schemes are integrated in this technique, which involves the use of beamforming imaging with local scanning, along with standing wave based full field laser scanning. Two types of excitation methods (guided waves and standing waves) are used for damage detection. The wave field measurements are obtained from a Laser Doppler Vibrometer and a mirror tilting device using a single fixed frequency (>80 kHz) excitation from a mounted piezoelectric transducer. First, a newly developed beamforming imaging technique with frequency-wavenumber filtering using local guided wave measurement is performed to identify the presence and location of damage in large structures without full field measurements. Once the damage is identified, the steady-state response based full field laser scanning is used to quantitatively assess the damage. Time variant and invariant feature based damage visualization is used to estimate the damage size and depth. To demonstrate the performance of the proposed technique, several experiments are performed on thin walled structures with several different types of damage. The results show that the technique is very effective in localizing damage and estimating depth with potential for the improved speed and performance.
An Approach on How to Determine Key Performance Indicators for Guided Wave Based SHM Systems Based on Numerical Simulation
Realization of an efficient SHM system for an arbitrarily shaped damage tolerant structure requires a clear understanding of what needs to be monitored. This may be determined through key performance indicators (KPI) being characteristics with respect to a tolerable damage to be monitored. Those characteristics can vary due to different reasons such as a structure’s geometry, the shape, size or type of the damage, the type of material and possibly other factors as well. To obtain an appropriate understanding of what a physical principle such as mechanical and hence guided waves does when travelling through a structure, numerical simulation can be of invaluable help. Starting from such a simulation the time domain signals to be monitored in practice can be generated that will subsequently be processed in a sequence of steps ending up in an artificial neural network (ANN) such that KPIs are derived that will then be used to realize an appropriate SHM system in practice. The sequence developed will be demonstrated along two examples, a first one being a plate with a notch grove through which guided waves are sent and the resulting signals are processed considering different algorithms looking at the time domain signal first and then moving onwards to difference of signals, Hilbert transform, oblique polarization filtering, and statistical methods like Auto-Covariance Function (ACF), Linear correlation coefficient, root mean square deviation and mean absolute error. Features of those algorithms are then used to train an ANN, which is finally due to provide the KPIs for signals recorded. The procedure is then applied to a more complex component being a riveted patched repair where it will be shown if the features determined from the simple plate with the notch grove can be simply transferred to the patch repair or what additional simulation work has to be done such that the KPIs can be identified accordingly. Based on these simulations an SHM system is then realized used for validation.
Wavelet Analysis of Diffuse Ultrasonic Waves for Damage Detection
Diffuse ultrasonic waves can be used to interrogate a large volume of complex structure with high sensitivity using a small number of sensors, but it is difficult to correlate changes in diffuse ultrasonic signals with the health of the structure being interrogated. This paper provides a wavelet-based comprehensive damage detection strategy for diffuse ultrasonic waves. They are generated by an impulse and tone burst and received by broadband piezoelectric transducers. The potential of the technique is examined, and laboratory examples are presented. It is shown that the wavelet carries information about the material damage. The strategy includes a systematic feature extraction method for the declaration of the structural status as "damaged" or "undamaged".
SHM using Guided Waves-Recent Activities and Advances in Germany
Not all issues that prevent Structural Health Monitoring (SHM) based on Guided Waves (GW) from being a part of today's monitoring solutions in industry are obvious to the scientific community. To uncover and overcome these issues, scientists working on SHM and GW problems joined in an expert committee under the patronage of the German Society for Non-Destructive Testing. An online survey among more than 700 experts and users reveals the hurdles hindering the practical application of GW-based SHM. Firstly, methods for proof of reliability of SHM approaches are missing. Secondly, detailed understanding of phenomenological described wave-damage interactions is needed. Additionally, there are significant unsolved implementation issues and unsolved problems of signal processing including handling of environmental influences. To enable substantial proof of reliability without unaffordable experimental effort also efficient simulation tools including realistic damage interaction are needed, enabling the joint use of experimental and simulated data to predict the capabilities of the monitoring system. Considering these issues, the committee focusses on simulation, signal processing, as well as probability of detection and standardization. In the presented work, recent activities of the expert committee starting with survey results are summarized. An open access data basis of life-like measurements is presented to allow testing and comparison of signal processing and simulation algorithms. Finally, a strategy for efficient proof of reliability increasing the acceptance of SHM in industry and for successful integration of SHM into real-world engineering structures is proposed.
Investigations on the Application of Actively and Passively Excited Guided Elastic Waves for the Monitoring of Fiber-reinforced Plastics
Nowadays fiber-reinforced plastics are found in numerous industrial applications. These materials have high tensile and flexural strengths and nevertheless a low density at the same time. The use of fiber-reinforced plastics is particularly relevant in areas where large masses have to be moved and accelerated, such as aerospace, railway and automotive engineering. However, testing and monitoring these structures is still challenge caused by the different damage behavior compared to metal structures. Nonvisible structural changes, such as delaminations and fiber-fractures, may cause local degradation and finally the failure of the components. In this work, active and passive ultrasonic methods based on guided waves are investigated for their applicability to carbon fiber-reinforced structures. Therefore, tensile tests with cyclically increasing load are carried out on specimens with different fiber orientations until complete failure. The acoustic emissions in the specimen during the load are recorded. As a second technique, actively excited guided waves are transmitted and received during the rest periods between the measuring ramps. Different parameters are extracted from the measured data, which allow a monitoring of the specimen’s degradation. Finally, a comparison of the results of the active and passive method is carried out addressing issues like the informative value and the sensitivity.
Passive Tomography by Elastic Guided Wave for Corrosion Detection in Pipelines
Corrosion presents a major challenge for various industries, especially for petrochemical and nuclear industry. Estimating accurate wall thickness maps of pipes or pressure vessels is of great importance for detecting corrosion damage in these structures and assessing its remaining lifetime. Guided wave tomography provides a solution for this problem in which pipe-like structures have a high diameter to thickness ratio, by sending guided waves through the region of interest, then using tomographic imaging [1, 2] to reconstruct the thickness map, significantly eliminating the need to take measurements at all points across the surface. The Structural Health Monitoring (SHM) process involves the observation of a structure over time by using measurements from sensors embedded in this structure in order to monitor its current state of health and hence taking at the right time corrective action to prevent further rupture or leakage. It is essential to use a small number of sensors to limit the intrusiveness of SHM system. Consequently, regularization is applied to adapt tomography in the context of the SHM system. The tomographic algorithm is evaluated in experiments. We show that this tomographic algorithm is particularly well adapted to a so-called â€passiveâ€ solution, where the ambient elastic noise which naturally presents in an operating structure (due to vibrations, aerodynamic turbulence) is exploited to make tomography by cross correlation [3], no need the emission of waves by the system. The complexity of the embedded SHM system is therefore reduced
Model Assisted Probability of Detection for Guided Wave Imaging Structural Health Monitoring
Guided Wave based Structural Health Monitoring has been studied in many laboratories in the last 20 years. In order to ensure industry adoption of this technology, a systematic performance demonstration of such systems is necessary. However, the cost of computing a Probability of Detection (POD) from experimental data is much higher in SHM than in classical nondestructive evaluation. This work presents the computation of a POD metric of a GW-SHM system, using a Model-Assisted POD (MAPOD) approach. The use of simulation enables in particular a large coverage of possible configurations and the creation of independent datasets. The simplistic studied application case is the inspection of an aluminum panel instrumented by 8 piezoelectric transducers for Guided Wave Imaging (GWI). The defect is a circular through hole. The POD is computed as a function of the defect size, taking into account the following variabilities: defect position and morphology, temperature of inspection, degradation of the sensors and measurement noise. In order to quickly compute the POD for various input parameter distributions, a meta-model of the configuration is built from simulation results obtained with the CIVA software.
Effect of Fiber Bragg Gratings Receivers’ Directivity on Guided Wave Tomography of Pipe
Structural health monitoring (SHM) consists in embedding sensors in a structure like aircraft fuselages, pipes or ship hulls in order to detect defects (for example cracks or corrosion in metallic materials or delamination in composite materials) before a serious fault occurs in the structure. Guided elastic waves emitted by a sensor and propagating to another one are often used as the physical way of detecting the defect. However, the implementation of SHM systems is restricted in many situations by the necessity to store or to harvest the electric energy necessary to emit the waves and also by the intrusiveness of the sensors. Guided wave tomography imaging is able to localize and quantify the severity of the defect when it comes to loss of thickness such as corrosion or erosion. However, it needs many sensors (generally piezoelectric - PZT - transducers) and it has a cost, particularly in terms of intrusiveness. The idea in this paper is to use less intrusive sensors such as fiber Bragg Gratings (FBG) to perform guided wave tomography. The inspected area of a pipe is surrounded by two rings, one with PZT and the other one with FBG.
Detection of Cracks in Turbine Blades with Nonlinear Ultrasonic Frequency Modulation
Non-destructive testing and structural health monitoring (SHM) techniques are becoming increasingly important for gas turbine manufacturers. Incipient cracks in the turbine blades have to be detected before catastrophic events occur. Linear ultrasonic methods are widely used to inspect structural integrity by monitoring the amplitude and phase variations of the mono-frequency input signal due to linear scattering caused by damage. However, closed cracks or small cracks cannot be easily detected due to a low impedance mismatch with the surrounding materials. Nonlinear ultrasonic methods have demonstrated the ability to detect early-stage damages. These methods investigate the distortion of the elastic waveform due to damage/material nonlinearity. This generates new signal components such as sub- and high-harmonics of the fundamental frequency in the frequency spectrum. The aim of this study was the development of a frequency modulated technique for the detection of cracks in turbine blades. Experimental work was carried out on flat samples with artificial defects and turbine blades excited by two different frequencies. A new global nonlinear parameter was used to determine a correlation between crack length and measured nonlinear features. The results were compared with samples without any defects. The findings showed a clear trend of increasing nonlinear parameters as a function of the crack size.
Selective Actuation and Sensing of Antisymmetric Waves Using Shear-mode Piezoelectric Transducers
One of the most advanced techniques for structural health monitoring is based on analysis of ultrasonic guided waves propagated through plate-like structures. Analysis of changes in propagated guided wave signals enables detection, location, characterization and quantification of damage. Characterization is possible because different guided wave propagation modes interact with various damage forms in different ways including reflection, attenuation, distortion, and mode conversion. An obstacle to guided wave analysis is the complexity of the received signals which can contain multiple guided wave modes that propagate with different velocities. The result is complex and superposed wave signals that are difficult to interpret. A common technique to deal with the multiple propagation modes of guided waves is to focus on the first wave packet arrival which has to be the fastest traveling propagation mode traveling the shortest path. At relatively low frequencies, the fastest mode of propagation is the fundamental symmetric Lamb wave propagation mode. Unfortunately, symmetric strain wave propagation is not sensitive to certain forms of damage. Antisymmetric wave propagation modes have been found to be sensitive to some different forms of damage, but are challenging to work with. At relatively low frequencies the fundamental antisymmetric mode propagates slower than the fundamental symmetric mode. Reflections, dispersion, and mode conversions create the potential for constructive and destructive interference in a signal that contains both symmetric and antisymmetric propagation modes. Some techniques have been developed to selectively excite and detect specific propagation modes to support signal analysis but many of these are inherently tuned to specific frequencies, require precise geometries, are inefficient, or employ multiple actuators or sensors.
Experimental Investigation of Crack Detection and Quantification Using PWAS Array
Crack detection and size estimation are two key functionalities of a structural health monitoring algorithm. An experiment of crack detection in an aerospace grade aluminum sheet metal alloys was designed using an array of piezoelectric wafer active sensors (PWAS). Gaussian-distribution based damage mapping algorithm was used for analyzing the guided wave signals. Since multiple modes of Lamb waves often complicate the guided wave scattering, this can pose a significant challenge for an algorithm’s functionality. Mode rejection using appropriate tuning of the PWAS was used to selectively excite a single Lamb wave mode. In this case, symmetric Lamb waves were excited in the structure. A pitch-catch experimental mode was utilized. The crack was detected, localized, and the crack size was estimated
Defect Sizing Using Convolution Neural Network Applied to Guided Wave Imaging
The lightweight aluminum alloys are extensively used in the aerospace industry. These materials are used for constructing complex structures such as aircraft fuselage due to their excellent strength-to-weight ratio, stiffness, and corrosion resistance. How- ever, defects such as corrosion or fractures can appear because of thermo-mechanical aging in a hostile working environment or impact forces due to the improper use of these structures. In light of this, Guided Waves (GWs)-based Structural Health Monitoring (SHM) system can be considered as a promising solution for the structural integrity screening, maintenance costs reduction and prolongation of the service time of these materials. In general, a sparse array of PZT transducers can be used for GWs exciting and sensing, and GWs Imaging (GWI) algorithms, such as Excitelet, can be used to process the measured signals. This imaging technique allows computing high-resolution images that represent the integrity of the structure, where each pixel of the image is mapped to the elementary portion of the structure and carries a Damage Index (DI) value. While defect presence and location can be determined from visual inspection of the image by naked eye, the defect sizing is a more complex problem due to non-linear behaviour of DI values regarding the defect size and its location. This paper proposes an approach for defect size evaluation. It relies on the extensive GWI database generated by means of Spectral Finite Elements modelling method implemented in CIVA and Convolution Neural Network (CNN) trained on numerical data. The CNN is used to build an accurate inversion model that takes GWI sample as input and determines the size of the defect. The model is tested on the simulated data and validated by means of experiment in aluminum plate.
Three-Dimensional Acoustic Wavenumber Spectroscopy for Structural Health Monitoring
Acoustic wavenumber spectroscopy (AWS) is an effective technique for the assessment of millimeter-scale damage on large thin-walled structures such as airplanes, wind turbine blades, ship hulls, and water tanks. To date, acoustic wavenumber spectroscopy remains a nondestructive evaluation technology, as it requires significant human intervention. This research demonstrates how including automated three-dimensional geometry scanning in 360-degrees can work to extend the capabilities of AWS to structural health monitoring. First, we collect threedimensional geometry for our wavenumber analysis over complex geometries. We perform this for three different samples: 1) A large plate oriented normal to the scanner, 2) Two plates clamped at an angle, and 3) An intentionally bowed plate. Then we perform wavenumber estimation on the structures correcting for perspective errors based on the geometry information. In the future, we plan to reduce human involvement during the data collection process.
Effects of Homogenization and Quasi-Isotropy Assumptions on Guided Wave-Based Nondestructive Testing Methods
Past research on guided ultrasonic wave-based methods for nondestructive testing of composite materials often assumed quasi-isotropy of composite laminates. Addition- ally, the quasi-isotropic laminate is homogenized and effective material properties for the laminate, modeled as a single layer material, are employed in subsequent analytical, numerical or experimental studies. However, deviations from the intended layup cause a directional dependency of the propagation velocities, thus requiring the modeling of the entire laminate. This not only alters wave fronts but it may also lead to significant errors in time-of-flight analyses for longer propagation ranges. Hence, in this paper, a comprehensive numerical-experimental approach is taken to investigate the quasi-isotropy of composite laminates in the context of guided ultrasonic wave propagation. A combination of semi-analytical and analytical tools are used to determine the dispersion curves and the propagation characteristics for several composite plate structures. The methods are applied for quasi-isotropic laminate that can be homogenized as well as a general composite laminate. The results are validated through dispersion experiments, and implications for damage detection are highlighted.
Bend Detection of Pipe Using Guided Waves
Modern industries rely heavily upon the pipeline infrastructure for the supply and distribution of required materials, which normally are high-temperature, high-pressure, explosive, inflammable gas/liquid, toxic, even radiant fluid. The pipelines usually achieve a very long operating life of 40–50 years. During their operation, pipelines can suffer from a wide variety of damage and aging defects. Some of the most common causes of failure in pipelines are corrosion, stress cracking, seam weld cracks, material flaws, and externally induced damage by excavation equipment. Stress rupture due to large deformation, such as bending under the external loading, is one of the damage in the pipe. Cylindrical guided waves based techniques have been proven to be effective and promising tools for the damage detection in pipes, in which guided wave can propagate along the span (axial direction) of the pipe at long distances. In this work, guided wave is employed to detect bend in the pipe. First, the propagation characteristics of guided waves affected by the bend is reviewed and analyzed. Numerical study is carried out to explore the detailed wave displacement, strain profile and so on. With the help of a special piezoelectric wafer, the newly generated modes due to the bend is detected by guided waves.
Hybrid Modeling of Elastic Wave Propagation: Domain Coupling and Stability
This paper reports on a formulation and analysis of a coupling scheme for elastic wave propagation modeling. Two local computational strategies are considered, namely, the Local Interaction Simulation Approach (LISA) and Cellular Automata for Elastodynamics (CAFE). While LISA can work only with rectangular cells, CAFE is capable of simulating waves in both triangular and rectangular lattices. Thus, a hybrid approach is of great interest since the non-uniform mesh capability of CAFE triangular cells can be readily coupled to LISA’s rectangular grids, taking advantage of the built-in LISA features on the uniform portion of the domain. This paper provides an insight into the formulation of LISA and CAFE and dynamic coupling of these techniques. The idea of the hybrid approach involves coupling of an overlapping interface between LISA and CAFE domains with a self-coupling process, wherein elastic and inertial forces are coupled dynamically. The self-coupling property of the hybrid scheme results in a non-obvious stability criterion for the explicit time integration scheme used for wave propagation. The coupling is analyzed analytically, so close-form expressions are found for finding the critical time step for the coupled model. Next, the coupled model is tested for different ratios of cell discretization lengths in LISA and CAFE, and the stability is analyzed for different ratios.
Evaluation of Disbonds by Diagnostic System Using Lamb Wave Sensor Network
Composite materials are used commonly for aircraft manufacturing, and the composite bonded structure is needed to improve flight performance in the future. In terms of maintenance for bonded structures, it is important to detect “disbondsâ€ accurately, and Structural Health Monitoring (SHM) system with lamb wave is one of the most promising methods for the damage detection in the composite structures [1], [2]. In this research, the detection capability of fatigue disbonds in the composite bonded structure was evaluated by the lamb-wave-based SHM system, SMART Layer® (Acellent Technologies, Inc.). In particular, the fatigue tests were conducted to grow the artificial disbonds on the boundary surface between the CFRP skin and T-shaped stringer structures. In order to evaluate the correlations between the disbonds growth and the changes of lamb wave signals, lamb wave signals were transmitted and received using a number of patterns of sensor networks.
Frequency Mixing Response of Ultrasonic Guided Waves in Plate
It has been shown that the use of frequency mixing response has some unique advantages over the nonlinear ultrasonic technique based on the higher harmonic generation, such as frequency selectivity, which can intentionally avoid receiving unexpected harmonic components induced by the instrumental systems. In addition, the feature of spatial selectivity of scanning wave mixing technique can readily be used to locate the region of damage. In this paper, frequency mixing response induced by the collinear interaction of two primary Lamb wave modes with different frequencies in an isotropic and homogenous plate is theoretically analyzed and experimentally observed. The second- and third-order combined harmonics generated by the collinear wave mixing of two primary Lamb waves propagating in a given specimen are analyzed, and meanwhile the existences of the combined harmonics at some specific frequencies are predicted. An experimental procedure is proposed to measure the combined harmonics that are induced by the collinear cross-interaction of the two specific primary Lamb wave modes at some given frequencies. The theoretical analysis and experimental observation provide a clear physical insight into the frequency mixing response induced by the collinear cross-interaction of two primary ultrasonic Lamb wave modes. Considering the difficulty for experimental verification for theoretical predictions in a systematic manner, characteristics of nonlinear frequency response for Lamb waves mixing are also examined numerically in this paper. Results indicate that the synchronism and symmetric property of selection of primary Lamb wave mode pair significantly affect the generation of combined harmonic waves at mixing frequencies. These numerical investigations clearly illustrate the physical process and nonlinear feature of Lamb waves mixing. It is shown that the controllable potential of mode pair selection for Lamb waves mixing, which is of practical significance for nonlinear acoustic scanning in large specimen.
Damage Localization Via Model Updating Using a Damage Distribution Function
With this work, we present the application of a damage distribution function to finite element (FE) model updating with the goal to detect, locate and quantify structural damage using a prestressed concrete tower as an example. The choice of design variables greatly influences the quality of the model updating procedure. Thereby, the parameterization connected to the design variables has to be able to model the structural damage with a high resolution. At the same time, the number of design variables should be as low as possible, since a high amount of design variables can result in an objective value space with many local minima, making numerical optimization unfeasible. A common approach consists of directly fitting the sectional stiffness of FEs to measured behavior. Thereby, the number of design variables increases linearly to the element count of the model. In order to reduce the number of design variables, they are commonly assigned to groups of FEs supposedly having similar mechanical properties. An additional method to even further reduce the amount of design variables is the determination of susceptible regions based on experience, prior knowledge or inspection findings. However, the position of damage is unknown in many applications. To alleviate this problem, we introduce a damage distribution function, which is described by few parameters. As these parameters represent the design variables of the optimization process, the proposed model updating procedure is independent of the FE mesh resolution as well as prior assumptions about the defect location. We demonstrate the application of the damage distribution function using a prestressed concrete tower as an example. First, we apply a three-parameter damage distribution function in order to detect, locate and quantify an artificially induced damage in a simple beam model of the considered tower. In this application, the defect location is determined along the height. Second, we extend the damage distribution function to a three-dimensional shell model of the concrete tower. This time, the location of the fictitious damage is also determined along the perimeter. The proposed parameterization keeps the amount of design variables low and enables a high numerical performance as well as stability, while maintaining the ability to detect, locate and quantify damage.
On the Integration of SHM and Digital Twin for the Fatigue Assessment of Naval Surface Ships
The integration of structural health monitoring data with multi-scale, multiphysics, probabilistic models can be used to help track the status of assets and aid in decision support. This is the paradigm laid forth by researchers working in the field of digital twin. The scope and complexity of this field is so large and so vast, that the development of the methodology will continuously evolve as the technology and capabilities monitoring devices and numerical models continuously progress. However, the implementation of a cyber-physical system, even with the limitations of current technology, can lead to essential information necessary to implement condition based maintenance for naval ship structures. The objective of this paper is to demonstrate the use of strain gauge data coupled with a global finite element model and a fatigue damage assessment model to provide information pertinent to maintenance and life cycle decisions. An illustrative example is presented and identifies the relative contributions of each step of the digital twin process to the overall uncertainty. This includes both epistemic uncertainties introduced through different modeling techniques, and aleatory uncertainties associated with the natural variability of structural capacity. The benefits of monitoring the ship and utilizing the digital twin framework is discussed, along with the challenges and limitations.
On the Reliability of Finite Element Models for the Training of Machine Learning Algorithms for Damage Detection in Bridges
In Structural Health Monitoring of bridges, machine learning algorithms for damage detection are typically trained using an unsupervised learning strategy, with data gathered from monitoring systems, and assuming the structures are undamaged and functioning under normal operational state conditions during a certain period of time. However, the absence of information regarding the structural response under more extreme environmental and operational conditions, and under damage as well, makes the distinction between these states very challenging, and may cause damage detection algorithms to yield false-positive indications of damage. In the worst case, environmental and operational variability may hide actual damage, yielding falsenegative indications. In order to overcome this limitation, the authors have recently proposed a hybrid approach for the training of machine learning algorithms. Rather than relying exclusively on measured data, the hybrid approach uses numerical models of the structure to generate data that corresponds to the undamaged scenarios that rarely occur in newly built bridges. This data is used for the training of the machine learning algorithms together with the monitoring data. While the hybrid approach was shown to successfully diagnose damage for a real bridge structure, the question of how good this performance would be in the absence of long-term monitoring data was left unexplored. Therefore, this paper pretends to assess the performance of a damage detection algorithm trained with numerical data only. Monitoring data is used only for the initial calibration of the numerical model, which does not need to be precise, since the probabilistic variation of uncertain parameters is taken into account.
Model Based Optimisation of Ultrasonic Corrosion Measurement
The detection and characterisation of corrosion in pipework is a significant challenge across a wide range of industries. A common inspection technique is the use of monolithic ultrasound probes to perform pulse echo measurements to characterise the remaining wall thickness. Although this is one of the most conceptually straightforward ultrasonic measurements, there are complicated interacting effects such as surface roughness, probe size, operating frequency and signal analysis method. The use of numerical modelling rather than experimental trials can accelerate the optimisation and qualification of the inspection. This paper presents results of applying an efficient sampling and interpolation algorithm that minimises the number of model evaluations required to build an accurate surrogate model of the inspection outcome. The application of sensitivity analysis to this model demonstrates that the inspection is insensitive to the dimensions of the probe and is highly sensitive to changes in the wall thickness and changes in the frequency properties of the probe, demonstrating the suitability of the inspection.
A Novel Double Beam Element with Continuously Distributed Elastic Springs for Modelling De-bonds in Adhesively Bonded Joints
A defect proof joint is not guaranteed by even the most sophisticated manufacturing techniques. Hence, health monitoring of joints is of utmost significance. Use of guided waves for non-destructive testing of adhesively bonded lap joints is addressed in this work. A double beam element is developed to model the adhesively bonded region. A double beam element is constituted of two parallel Timoshenko beam-rod elements connected to each other by continuously distributed vertical elastic springs. Only a single spectral element is sufficient to model joints having varying levels of adhesion provided the adherands are crack free. Alongside, a superconvergent finite element is also formulated to model the bonded region. The superconvergent finite element developed in this work uses a combination of polynomial and exponential functions as shape functions, which results in exact stiffness matrix, and thus reduces the approximation errors significantly in comparison to conventional finite element formulation. The superconvergent double beam element is also free of shear locking as the order of interpolating function of transverse displacement is one order higher than that of the beam slope. Further, responses of single lap joint samples with varying levels of adhesion subjected to ultrasonic loading are obtained. Thus, the numerical models developed here can be used to conduct preliminary studies on adhesively bonded joints with a wide range of loading frequencies, and aid in selection of probing frequencies for conducting experiments.
Validation of Eddy Current Finite Element Scheme for Non-destructive Evaluation Problems Without Removing Coatings
Intelligent Processes and Operational Monitoring of Composite Systems
Advances in the design, development, and applications of multifunctional materials is an important aspect of 3rd generation material structures. Design and process fabrication provide critical linkages between the intent and operational characteristic of man-made structures. The research community has gravitated towards knowledge-based design, as a means to intelligently and concurrently manufacture device structures. In this work, we present the congruence of i) process and ii) operational data, as a construct of the digital twin. A multifunctional composite is created through an integrative multi-material additive manufacturing (AM) approach, for monitoring the composite fabrication process and integrating sensors thereby providing enriched datasets that can be traced to each individually manufactured product. Linkages of the process-property-performance relationships will be studied and highlighted as a digitally driven discovery for improving additive manufacturing capabilities to produce composite structures with customized, inexpensive and smart 3D printed molds. Uniquely, concepts of IIoT and industry 4.0 drive the manufacturing, data collection and streaming of smart material composites. i.) Simultaneous manufacturing between cooperative robots enables a higher degree of complexity with multi-material deposition and processing. Data collection from printed sensors can profile a part, from start to finish, for individualized quality assurance and life cycle monitoring with 33% retained performance after repeated use. ii.) An inkjet printed curing sensor providing in situ process analysis throughout the liquid composite mold (LCM). Real-time cure rate monitoring facilitates informed manufacturing of customized composites enabling accurate failure prediction/prevention and reduces non-value-added time by automating the process. It is shown that 10 hours is sufficient to obtain a complete cure of the tested resin. A hybridized approach to novel composite manufacturing is made possible by the intelligent integration of dissimilar smart materials, bridging the gap of function and form in one tooling component for composite fabrication with the aid of cooperative automation.
Developing Intelligent Structures and Devices Using Novel Smart Materials and Multi-Material Multi-Method (m4) 3D Printing
The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid fabrication of complex structures previously unrealizable with traditional manufacturing techniques. Current approaches, however, are limited to single materials or single methodologies greatly limiting the potential scope of manufacturable products and components. Recently, our group has developed a novel multi-material multi-method (m4) 3D printer which integrates four AM technologies and two complementary technologies into one single platform. This allows for the fabrication of complex devices able to provide a wide range of functionalities ranging from stretchable electronics to self-sensing devices. To demonstrate these functionalities in the realm of printable electronics, multiple proof of concept printed circuit boards (PCBs) were fabricated which solve issues commonly encountered in 3D printed electronics such as high resolution or vertically integrated access (VIA) circuits. In addition, 3D printed smart structures able to respond to external stimulus, such as light or heat, have become highly desirable for applications ranging from soft robotics to implantable medical devices. Recently, our group has turned to liquid crystal elastomers (LCE), a class of active material able to generate large, rapid, and reversible actuations. Therefore, using the m4 3D printer, LCE-based smart structures requiring complex electronics were fabricated which can change their shape in response to an applied current. To demonstrate this, a smart, reconfigurable radio frequency (RF) antenna was 3D printed which can change its shape and operating frequency as a function of the applied current. These examples demonstrate the vast potential of m4 3D printing for creating smart, reconfigurable, and multi-functional structures.
A Flexible and Lightweight Impact Monitoring System with Ultra-low Power Consumption for Aircraft Smart Composite Skin
Aircraft Smart Composite Skin (ASCS) which has the capability of Structural Health Monitoring (SHM) for large-scale aircraft composite structures to monitor impact needs to be developed because the poor impact resistance of composite structures may lead to barely visible impact damage and result in stiffness degradation and a significant loss of structural integrity. However, the impact is an instantaneous event so that it needs to be monitored on-line continuously during the whole service lifetime of the ASCS. Thus considering the strict restrictions of aerospace application, an impact monitoring system and sensor network is required to be low weight, low power consumption and high reliability. In this paper, an impact monitoring system with advantages of compactness, ultra-thin, light weight, ultra-low power consumption and high efficiency is developed to meet the strict requests of on-line application. Differently from conventional impact monitoring systems, the complex circuits are greatly simplified and the whole impact monitoring process is achieved in a digital way by turning the output of the piezoelectric sensors directly into digital sequences through a two-level digitalized mechanism which is realized by combing diode array with a Micro-Process Unit (MPU). A simple but efficient impact-region localization method is implemented in the MPU. In addition, the impact monitoring system is realized in a flexible way so that it can be embedded onto the composite structure to be a mechatronic system to realize impact monitoring on-line continuously. To illustrate the capability of the impact monitoring system, it is integrated with a flexible piezoelectric sensor array to construct a large-scale impact monitoring sensor network system. The network system is integrated on the surface of a large-scale stiffened carbon fiber reinforced plate to become an ASCS and the impact monitoring ability of the ASCS is validated.
Performance Comparison of Improved Particle Filters for On-line Fatigue Crack Prognosis
Fatigue crack growth prognosis is a fundamental task for ensuring structural integrity. Recently, attentions have been gradually paid to methods that combine on-line measurements of structural health monitoring (SHM) with the particle filter based prognostics. However, most studies used the basic particle filter algorithm, which has the intrinsic particle impoverishment problem. There are different kinds of improvements made to the basic particle filter in the field of particle filtering. Few studies have been carried out to discuss their applicability to on-line fatigue crack growth prognosis combining with the SHM technique. Therefore, this paper compares four different improved particle filter algorithms for the on-line application of fatigue crack growth prognosis by integrating the guide wave based SHM. Studies are carried out on the basis of fatigue tests performed on the attachment lug, which is a key structural element in engineering structures. Two cases are involved that the SHM measurement has relatively large or small errors, which are influenced by whether the measurement equation is accurately trained with historical data. The prognostic accuracy of these improved particle filters is discussed. Moreover, effects of the particle number on the performance and computational cost are analyzed.
A Novel Impedance-based Method for Structural Monitoring of Automotive Lithium-Ion Battery Packs
The high-voltage battery represents the key component of electrified automotive drive-trains. State-of-the-art lithium-ion cells (LIC) are commonly used due to their high specific energy. The cells are stacked and reinforced by a rigid frame. In addition to mechanical loads during operation, this substructure must also withstand inherent stresses caused by the swelling of the cells. A mechanical failure of the frame results at best in a vehicle breakdown but may also cause a short-circuit and catastrophic thermal propagation in one or more cells. Here we propose a novel method to monitor cell modules in-operando based on the measurement of the cell impedance. There are various sensitivities of the impedance, such as regarding temperature or State-of-Charge, but, as will be shown, it is also influenced by external mechanical loads. Two experimental setups are conducted. First, a simple test is presented during which the phenomenon was initially observed. Secondly, another experiment is carried out to resolve the presumed sensitivity across relevant compression forces. The measurements confirm the initially observed phenomenon. Finally a brief perspective for an effective and industrializable monitoring system is given.
Demonstration of Prognostics Health Monitoring (PHM) in Adhesive Lap Joints using Simulated Studies
Adhesively bonded joints are increasingly used in structural applications due to many advantages over classical mechanical fasteners. However, they are susceptible to fatigue damage due to the hostile working environment. It is essential to detect, quantify the damage and estimate the remaining useful life (RUL) under fatigue loading. This paper presents prognostics health monitoring (PHM) framework that can quantify the damage and estimate the RUL in adhesive lap joints. A PHM framework is the synthesis of four disciplines: damage diagnostics, predictive modeling, uncertainty quantification, and uncertainty propagation. Damage diagnostics provide the damage evolution in terms of damage growth rate as input to PHM system. In this work, a new diagnostic method based on ultrasonic Lamb waves is proposed for in-situ measurements of crack length in a single lap joint (SLJ). The idea is to excite single mode using two piezo transducers and extract the wave packet reflected from the crack tip to estimate crack length. The proposed method is verified using computational simulations. A predictive model must be capable of simulating the damage growth physics and can govern the growth rate using model parameters. In this study, the cohesive zone model (CZM) is used to simulate crack growth in SLJ. Uncertainty quantification methods require the evaluation of the predictive model for large parameter sets. To achieve this, setup is built using Python script to run crack propagation simulations in ABAQUS. Convergence issues and computational challenges associated with the setup is addressed. Finally, the procedure to estimate RUL using diagnostics data, predictive model and uncertainty quantification methods is discussed.
The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics, Prognostics in Aerospace Structural Health Monitoring
Structural Health Monitoring (SHM) defined as the process that involves sensing, computing and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT project proposed is to develop a framework that connects sensor data with real-time processing to provide diagnostic/prognostic capabilities. Specifically, the proposed IIoT model is comprised of 3 components: the Cloud, the Fog and the Edge. The Cloud is used to store historic data as well as to perform demanding computations such as remaining useful life estimations. The Fog is the hardware that performs prognosis using information received both from sensing and the Cloud. The Edge is the bottom level hardware that filters data at the sensor level. In this investigation, an application of this method that uses multiple sensors to evaluate the state of health at laboratory conditions namely, acoustic emission, digital image correlation, and infrared thermography is presented. The key link that limits human intervention through data processing is the implemented database management approach. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. In addition, the algorithms used capable to execute filtering followed by classification at the Fog level, as live data is recorded by the used sensors. The processed data is automatically sent to the Cloud for remaining useful life estimations and to perform forecasting.
Health Prognostics of Lithium-ion Batteries and Battery-Integrated Structures
This work developed and evaluated an acousto-ultrasonic system that measures battery state of charge and, in particular, its state of health using a built-in, low-profile piezoelectric transducer system. A diagnostic method was proposed that relates changes in the guided wave signals to the charge, discharge, and aging processes, via electrochemically-induced changes in mechanical properties. A matching-pursuit-based feature extraction scheme was developed to allow an efficient, in-operando decomposition of signals into a set of predictors correlated with battery states. A particle filter framework was established which provides state estimation and remaining life prognostics to allow ultrasonic features to be used as measurements. It was shown on off-the-shelf Li-ion batteries and battery-integrated structures that the ultrasonic technique significantly improved the prediction performance and contained uncertainties.
The new generation of Industrial Internet of Things (IIOT) will transform the Industrial Age to the Information Age by exploiting opportunities and benefits offered by Information Age technology and techniques. Assets and infrastructure continue to drive innovation in systems and deployment of IOT technologies that have the potential to deliver new capabilities and cost savings. A key enabler for IIOT are sensors and data procured from them. Sensor based IOT devices can gather more data, facilitate more complex analysis and faster reactions, and reduce human error, delivering more precise and efficient military capabilities. Sensor network based Structural Health Monitoring (SHM) systems have the potential to provide real-time and historical sensor based data on the integrity of any structural platform to enable IIOT. SHM systems can reliably detect, localize and quantify damage in components in existing and new structural assets and use the identified damage to make a decision on when to remove the component or for prognosis of the remaining useful component life and system performance. Platform readiness, minimization of costs from unnecessary teardowns, as well as enhanced safety are some of the major goals of SHM. This paper provides examples of SHM systems developed for use in various platforms for in-situ and remote sensing. In-situ on-board sensing, off-board sensing and data transfer along with remote data collection process over long distances for multiple platforms are discussed. Key components will be identified and discussed. Data analysis and data management for multiple platforms to enable IIOT are presented.
Validating Accelerometers for Rotating Machinery in Structural Health Monitoring Applications
Improving safe operation of rotating machinery by using structural health monitoring (SHM) relies on robustness and reliability of the sensors being used. Prior analysis of single components is crucial, particularly if the system is mounted in remote and inaccessible areas. In our approach, a three-step process was pursued to evaluate accelerometers in SHM applications: accelerometer selection, protective coating evaluation and characterization of sensors before mounting. Uncertainty analysis of accelerometers was used for selecting components comprehensively. The influence of protective coating was evaluated by analyzing eigenfrequencies of the coating volume. Sensors were finally validated with two setups inspired by the properties of rotating machinery. The approach highlights the importance of controlled component selection and reasonable characterization of sensors to increase the benefit of SHM in rotating machinery.
Evaluation of Structural Health Monitoring Sensors During Fatigue Loading at Room Temperature
This paper focuses on exploring the repeatability of Structural Health Monitoring (SHM) sensors to detect fatigue cracks emanating from crack-starter notches in 0.063 inches (1.6 mm) thick, aluminium 7075-T6 panels, measuring 12 by 24 inches (304.8 by 609.6 mm) in a room temperature environment. Three panels were tested in total, where each panel had three 0.25 inches (6.35 mm) diameter holes, drilled at 3 inches (76.2 mm) apart along the vertical centre of the plates. Two outer holes had electrical discharge machining notches for starting fatigue cracks. Four different SHM technologies: acoustic ultrasonic, National Research Council developed crack foil sensor, resistance wire strain gauges, and thermoelastic stress analysis, were evaluated for their ability to repeatedly and reliably detect growing fatigue cracks. Repeatability in the context of this work is defined as using the same SHM sensor systems on identical test coupons to see if the same level of detection and/or trend could be achieved.
Detection Sensitivity Analysis for a Potential Drop (PD) Structural Health Monitoring (SHM) System
The paper presents a detection sensitivity analysis for a novel approach to monitoring fatigue crack growth. A carbon nanotube (CNT) sensor was used based on a potential drop (PD) damage detection strategy. Resistive CNT film was laminated between impermeable membranes to create a durable crack gauge that can resist high strain levels, high temperatures and submersion in water. Any crack growth below the sensor would disrupt the CNT electrical entanglement, therefore increasing the network resistance. As opposed to traditional crack gauges with discretized output based on broken copper traces, the CNT crack gauge provides for a continuous range of output, with resistance change proportional to the square of the crack length. The purpose of the present study was to evaluate the sensitivity of this CNT sensor to damage size using statistical approaches recently developed specifically to be applied to Structural Health Monitoring (SHM), including the Length at Detection (LaD) and REpeated Measured Random Effects Model (REM2) techniques.
Generating Viable Data to Accurately Quantify the Performance of SHM Systems
Reliable structural health monitoring (SHM) systems can automatically process data, assess structural condition and signal the need for human intervention. There is a significant need for formal SHM technology validation and quantitative performance assessment processes to uniformly and comprehensively support the evolution and adoption of SHM systems. In recent years, the SHM community has made significant advances in its efforts to evolve statistical methods for analyzing data from in-situ sensors. Several statistical approaches have been demonstrated using real data from multiple SHM technologies to produce Probability of Detection (POD) performance measures. Furthermore, limited comparisons of these methods - utilizing different simplification assumptions and data types - have shown them to produce similar POD values. Given these encouraging results, it is important to understand the circumstances under which the data was acquired. Thus far, the statistical analyses have assumed the viability of the data outright and focused on the performance quantification process once acceptable data has been compiled. This paper will address the array of parameters that must be considered when conducting tests to acquire representative SHM data. For some SHM applications, it may not be possible to simulate all environments in one single test. All relevant parameters must be identified and considered by properly merging results from multiple tests. Laboratory tests, for example, may have separate fatigue and environmental response components. Flight tests, which will likely not include statistically-relevant damage detection opportunities, will still play an important role in assessing overall SHM system performance under an aircraft operator’s control. One statistical method, the One-Sided Tolerance Interval (OSTI) approach, will be discussed along with the test methods used to acquire the data. Finally, prospects for streamlining the deployment of SHM solutions will be considered by comparing SHM data needs during what is now an introductory phase of SHM usage with future data needs after a substantial database of SHM data and usage history has been compiled.
Long Term Strain Monitoring and Management System of Retrofitted Bridges
A retrofitted/strengthened structure faces a challenge on how to suitable evaluate structural performance and serviceability, because of the structural parameters and the features might be changed after the retrofitting. Additionally, it is difficult to evaluate structural performance because of the possible unclear structural damages, e.g., material degradation, changed structural internal stress distributions. Therefore, we need a sensitive index, that can directly show the structural performance, when we design a monitoring system for a retrofitted structure. This work aims to assess the performance of a real strengthened bridge by using an area sensing system via long-gauge optic fiber sensors. The area sensing system was installed on the bridge before the strengthening project, and it contained 4 -sensing-areas which covered four different key areas of the bridge. From the area sensing system, the strains distributions throughout the important areas of the bridge were obtained, and the strain responses before and after the strengthening were compared to effectively evaluate the strengthening effect. Furthermore, the strain responses towards traffic loading were continuous monitored to evaluate the structural behavior after strengthening. Information and statistics on daily, monthly and annual traffic amount were provided. The typical strain responses of the difference area, such as undamaged and strengthened-damaged area under different vehicles were discussed.
Reliability of Lamb Wave SHM Systems: Influence of Hydrostatic Pressure, Mechanical Loading and Fatigue on the Value of a Damage Index
Structural Health Monitoring (SHM) systems can cause changes in paradigms in industrial maintenance, such as a move from time-based to condition-based maintenance, where the integrity of a structure would be continuously monitored and analysed. Lamb Waves are one of the most promising technologies for SHM since they are able to monitor a relatively large area with a relatively small number of sensors, especially when compared to other techniques such as conventional ultrasound and eddy currents. However, there is a need to evaluate the reliability of such systems under operating conditions, so there are several research programs which aim to investigate their response variability when faced with temperature changes, mechanical load cycling and other physical effects. This study is part of a collaboration program between LAMEF and Embraer R&D department. The current paper will present initial results and discussions on the influence of different scenarios on lamb waves systems. Several laboratory tests have been conducted in order to evaluate the influence of conditions commonly imposed on aircraft structures on the signals, such as tension, compression, hydrostatic loads (found in fuel tank). Additionally, a study of structure settling under a cyclic loading was performed. The results show that each kind of load has a different influence on the signals, and it was also observed that the influences may change depending on excitation frequency.
Sensor Reliability in Extreme Environments for Asset Monitoring
Asset monitoring is a critical factor for a wide variety of commercial assets where accidents due to structural usage, structural fatigue and impact damage are relatively common. Flexible multi-functional sensor layers can conformably distribute sensors over very large areas and enable availability of instantaneous information on the structural integrity of the asset, measure environmental conditions for optimum performance while adding minimal weight. Key factors in enabling reliable monitoring using the sensor layers are 1) Accurate and fast installation on platforms of any size and shape 2) Survival of the sensors in extreme environments that the asset is subjected to 3) Self-diagnostic capabilities to ensure accurate functioning of the sensors and 4) Low-cost of repair/replacement This paper will provide insights into new developments in the reliable usage of multifunctional sensor layers on assets in extreme environments in the field. Details on new methods developed to minimize installation time, protective coatings for survival, novel designs for low-cost installations, Mil-Std testing etc., will be discussed. Field testing conducted on installations in extreme environments faced by applications such as embedded pipelines, mines, aircraft etc. and related results will be presented.
A Large-scale and Lightweight Piezoelectric Sensor Network for Aircraft Smart Composite Skin
Aircraft smart skin technology requires the integration of large-scale, lightweight and integrative sensor networks in aircraft structures, but it’s difficult to directly manufacture such networks at a large-scale, low-cost and simplified process. To study this issue, a large-scale and lightweight piezoelectric sensor network is developed in this article. The key principle is to manufacture the sensor network in a limited scale and expand it in a large scale for aircraft applications. The whole manufacturing process is realized by the mature Flexible Printed Circuit (FPC) process, which can greatly reduce the manufacturing costs, simplify the process flow and improve the yield and stability of the sensor network. Serpentine-based fractal island-interconnect structure design combined with the FPC process is proposed to ensure the excellent stretchability of the network. It can be uniaxial stretched to 500% of its original size and works well when fully expanded to cover 2500% of its original area. Experimental results show that the network can be applied in active and passive Guided Wave (GW)-based Structural Health Monitoring (SHM) of composite structures.
Experimental Verification of the Local Damage Detection Method Based on Spatial-temporal Correlation in Dense Sensor Networks
Structures get local damaged during their daily service or natural disaster like earthquakes and hurricanes. Local damage will gradually develop and grow, which will lead to the overall damage of the structure, and even lead to the collapse. Therefore, local damage detection plays an important role in ensuring structural safety and lifecycle maintenance of structures. In response to this phenomenon, the paper propose a new local damage detection method based on spatial-temporal correlation in dense sensor networks without the need for exact knowledge of structural properties. Firstly, the structural response is collected by sensor networks. The collected response is then used to analyze spatial-temporal correlation between the responses of different points. The damage indicator in the paper is called the angle coefficient, which measures the angle between regressed plans from two different states of the system. To ensure accurate detection of local damage, coefficient of determination is used in the paper to evaluate the reliability of angle coefficient, and chooses angle coefficients with higher coefficient of determination to detect structural damage. The performance of proposed method was verified through a dynamic excitation experiment on a scaled model of twostory assembled frame structure. The frame structure was constructed at the State Key Laboratory for Disaster Reduction in Civil Engineering at Tongji University. There are 7 cases in the experiment, including three different damage degrees. According to the structural response collected from the experiment, the angle coefficient is calculated to detect local damage in the system. The results show that the proposed method can successfully detect local damage and its location in the structure, even at a complex environment. Comparing influence coefficients of different damage degrees, it can be found that there is a certain positive correlation between angle coefficient and damage degree of the structure. That is, the greater the damage degree is, the higher the angle coefficient is.
Materials Communicating with the BIM: Aims and First Results of the McBIM Project
This paper introduces McBIM, a French national research project working on on-line monitoring on concrete structure. This project gathers 3 french laboratories (the CRAN, LAAS and LIB) associated with one company (360SmartConnect/FINAO SAS) that are working together from 2018 to 2021. It proposes 1) to design a “communicating concreteâ€, made of concrete equipped with embedded low-energy wireless microsensor network, able to manage and exchange data using BIM standards, and 2) to demonstrate the usefulness of this approach across several building lifecycle phases, namely the manufacturing, construction and exploitation phases for structural health monitoring. The paper will first start by introducing the project and then presents some preliminary results.
Shaping the Future of Structural Health Monitoring with IOT Sensors
Structural health monitoring (SHM) is an innovative method of monitoring structural safety, integrity, and performance without affecting the structure itself. Wireless sensor Networks (WSN) have demonstrated their potential for structural Health Monitoring: flexible, easy to deploy, lightweight and cost-effective. However, in many monitoring system, the conventional usages of WSNs are cases with low data rate, small data size, low duty cycle, and low power consumption. Structural health monitoring requires high data rate, time-synchronization, large data size, measurement stability and a relatively high Sampling rate. There are few studies about clock distribution in WSN for data fusion and modal analysis. In this study, we outline the synchronization requirements of wireless sensor networks and present how clock-drift, network aggregation capacity and measurement stability issues are solved.
Phased and Synchronous Sampling Between Multiple Smart Network Sensors for Modal Assessment of Large Structures
Operational modal analysis and vibration-based damage detection of engineering structures have become important issues for Structural Health Monitoring (SHM) and maintenance operations. For this task, embedded wireless platforms such as the PEGASE platform are appealing and suitable to collect vibration data and then perform off-line and remote computation easily. To obtain detailed modal information of large and very large structures, many sensors would be required to cover the geometry of the structure with a reasonable accuracy. However, when only a limited amount of sensors is available, large structures can be measured in several sensor setups, where some sensors remain fixed and some are moved between different measurement setups. With the sensors connected to different wireless platforms, the synchronous acquisition of data is required. In this paper, a solution of data acquisition synchronization, as well as signal processing for merging the information taking into account the change of sensor positions and environmental variability is presented.
Learning Collaborative Duty Cycling Strategies in Energy-harvesting Sensor Networks
Energy harvesting wireless sensor networks are a promising solution for low cost, long lasting structural health monitoring applications. Many common applications of these networks are fundamentally concerned with detecting and analysing infrequently occurring events. To conserve energy a subset of nodes can assume active duty, listening for events of interest, while the remaining nodes enter low power sleep mode. Judicious planning of the sequence of active node assignments is needed to ensure i) the active nodes can successfully detect events, ii) as many nodes as possible can be reached upon detection, and iii) the system maintains capability in times of low energy harvesting capabilities. We propose a novel deep reinforcement learning agent which acts as an autonomous centralised power manager for the system. We develop a simulation environment to emulate the behaviour of an energy harvesting sensor network. We then train the proposed agent to learn optimal node selection strategies through interaction with the simulation environment. The performance is tested on unseen historical solar energy data. The agent is shown to outperform baseline approaches on both seen and unseen data. Analysis and visualisation of the system behaviour is provided to illustrate the learned strategies.
Wirelessly Powered and Battery-Free LoRaWAN Wireless Sensing Nodes Designed for Communicating Reinforced Concrete
A Wireless Sensor Network dedicated to Structural Health Monitoring applications in harsh environments, which is the basis for the design of a communicating reinforced concrete, is presented. An autonomous wirelessly powered and battery-free LoRaWAN Sensing Node is highlighted here. The presented system measures temperature and humidity from its environment and transmits the recovered data to a Communicating Node, which interconnects the physical and digital worlds whilst simultaneously powering the Sensing Nodes via a far-field Wireless Power Transmission interface.
Offset Tracking of Sensor Clock Using Kalman Filter for Wireless Network Synchronization
Wireless Sensors Networks (WSN) are more and more used in structural health monitoring applications since they represent a less expensive and non-invasive way to monitor infrastructures. Most of these applications work by merging or comparing data from several sensors located across the structure. These data often comprise measurements of physicals phenomenons evolving with time, such as acceleration and temperature. To merge or compare time-dependent data from different sensors they need to be synchronized so all the samples are time-stamped with the same time reference. An initial synchronization of the sensors is needed because sensors are independent and therefore can not be all started at the same time. Subsequent re-synchronizations are also needed since the sensors keep track of time using their imperfect local clock. A quartz clock will drift in time due to the sensitivity of the quartz oscillator to its environmental conditions ; thus, synchronization accuracy depends on the quality of the oscillator, environmental conditions, re-synchronization frequency and time reference quality. The required accuracy of the synchronization depends on applications, for instance, one needs millisecond accuracy to analyze vibration data, microsecond for acoustic data and nanosecond to time-stamp electromagnetic propagation. Sensors can be synchronized by exchanging timing information as in the RBS [1], TPSN [2] and FTSP [3] protocols or through an external timing reference such as the PPS signal transmitted by the GPS as in [4]. While the first option might be less power hungry, our work in this paper focus on the second option as it allows for a better synchronization accuracy. This paper presents a smart-sensor able of time-stamping samples as well as measuring its clock offset and frequency from a noisy PPS signal. We implemented it on an FPGA to get high speed counters without software overhead. We then used a Kalman filter to track the offset with more accuracy and to adjust the sample time-stamps of the sensors. This work is aiming at having the highest time accuracy as possible while minimizing its consequence on sensor power-consumption.
Influence of Production and Environmental Parameters on the Behavior of Hybrid Piezo/Temperature Sensors for Production and Structural Health Monitoring of Composite Structures
During the last years the authors developed a novel hybrid multi-functional piezo / temperature sensor concept for flow front, cure and structural health monitoring of composite parts thus allowing monitoring of the production and the usage with the same sensor type. The sensors and the overall monitoring concept was validated on different composite parts like flat plates, a PAX door edge-member and a helicopter component. During the production of the sensors and the validation of the different composite parts slight differences in the behavior of the different sensors was observed. Within this paper the authors present the effect of imperfections in the production of the sensors and the effect of the production and usage environment on the sensor behavior. Both FE simulation based and experimental results were used to assess the influence of a variety of parameters such as: geometric variations during sensor production, misalignment of the piezo disk, the effect of temperature dependent behavior of the electric components such as piezo disk and PT100 or the effect of temperature and pressure on the sensor characteristic and subsequent performance during production and usage monitoring.
Mechanoluminescent-Perovskite Pressure Sensor for Structural Health Monitoring
Here we report a highly sensitive and robust flexible thin film pressure sensor realized through the novel combination of mechanoluminescent (ML) material with perovskite. This thin film sensor is constructed through a low-cost and simple manufacturing technique. In this work, we adopted a lateral type design of a thin pressure sensor primarily consisting (i) a sensing layer of copper-doped zinc sulfide (ZnS:Cu)/polydimethylsiloxane (PDMS) composite and (ii) a light absorbing layer of perovskite. Atop the perovskite layer lies a thin Au layer acting as the electrodes. ZnS:Cu is one of the highest intensity light emitting material among all inorganic ML crystals. The ML intensity increases with the increase of applied pressure without requiring any irradiation for ML recovery, which indicates that the self-recovery of the ML in crystals takes place by trapping of drifting charge carriers in the presence of a piezoelectric field. As such, this crystal can be suitable for durable sensor material. In order to manufacture a stable sensor, a mixed halide perovskite is used as a light absorbing material. The electrical current of the pressure sensor increases with the increase of applied pressure. The sensor has the application in structural health monitoring of large composite structures. This work shows a novel approach to nextgeneration sensors and is a promising candidate for pressure sensitive applications
Development of a High Temperature Ultrasonic SHM System Using Glass Couplant
Traditional in-service inspections of critical components can require lengthy plantshutdowns with very high associated costs. There is an increasing impetus to develop permanently-installed structural health monitoring (SHM) systems with the performance to replace conventional qualified ultrasonic inspections in the detection and sizing of defects of concern. This paper presents the progress made by Wood using high temperature (<450°C) transducers to construct a SHM system, and specifically a novel bonding method that has been developed to overcome the limitations of attachment by welding and clamping. Data is provided about ultrasonic performance, bond strength and the outcome of temperature cycling trials. The results of the practical work show that the glass bonding agent is likely to be a viable high temperature couplant, which opens up new possibilities in fixed transducer SHM system design.
Failure Monitoring of Bolted Joints by Using Eddy Current Array Sensing Film
Bolted joints, the most important connecting type for in-service aircrafts, plays a significant role for aircraft structures because of their strong bearing capacity and high reliability. Nevertheless, due to large/complex load, stress concentration around the hole-edge and multi-bolt distribution, there exist three typical fatigue failure modes for the joint layer of bolted joint, including shear-out failure, net-section failure and bearing failure. It is of great significance to estimate the failure mode for checking structural integrity and ensuring structural safety. In this paper, a novel flexural eddy current array sensing film produced with printed circuit technology is proposed to monitor the potential failure of joint layer. The eddy current array sensing film consists of an exciting coil and multiple sensing coils distributed in the axial and circumferential directions. The circumferential distribution of sensing coils is expected to identify the circumferential location of failure to estimate the failure mode, because different failure modes have different circumferential locations. Finite element simulation and experiment were both implemented to prove that the proposed eddy current sensing film is capable of differentiating the net-section failure, shear-out failure and bearing failure, and tracking its growth.
Evaluation of Commercially Available Sensors for Soil Stress Measurements
The interaction between the substructure and its surrounding soil is a crucial consideration in structural health monitoring. In locations with seismic activity, many structures are susceptible to damage or failure due to soil liquefaction. Determining the horizontal stresses in soil before and during cyclic loading, which is a fundamental challenge in geotechnical engineering, has significant consequences on interpreting liquefaction susceptibility and predicting foundation response to earthquake loading. Measuring this value will help verify or modify previous assumptions made, and correct values can be incorporated in engineering calculations. The applicability of various existing sensing devices and concepts for measuring horizontal stresses in soil are investigated. Four commercially available pressure transducer sensor systems are evaluated when buried within a soil specimen. The evaluation of results showed that none of the sensors functioned well. Major limiting factors included hysteresis and nonlinearity in results, unrepeatability of tests, and placement of the sensor within the sample. More technologically advanced sensor concepts, such as an active control loop design or active expanding diaphragm type sensors seem inevitable for measuring horizontal stresses in soil.
A Thickness Monitoring Solution for Pipeline Integrity Management and Corrosion Detection
The aim of this study was to investigate a pipe wall thickness monitoring technology that enhances the structural integrity of ferromagnetic steel pipelines. The pipeline integrity management system presented was able to identify the changes in wall thickness. This will lead to development where the occurrence of corrosion in pipes can be detected, aiding operators to assess asset condition and prevent failure at an early stage, providing a greater control over the operation and performance of the facility. The system consists of a sensor which uses an inductor to monitor the changes in inductance in the pipe wall, a permanent magnet that magnetically penetrates the pipe wall thickness and a PCB with a microcontroller and wireless connectivity. An algorithm was developed to translate the captured inductance into thickness measurements. The data was wirelessly transmitted in real-time and was accessed via a user-friendly app. The system was tested on ferromagnetic materials of different thickness, ranging from 1mm to 12mm. The results confirm that the inductance measured is a function of the material thickness that is able to detect a thickness loss down to 1mm. The system was developed with the goal of being permanently installed on areas of high erosion/corrosion risk and provides thickness measurements when interrogated remotely.
Design of Embedded Wireless Sensors for Real-Time and In-Situ Strain Sensing of Fiber Reinforced Composites
The increasing demand of early detection of barely visible damage inside fiber reinforced polymer (FRP) composites has led to the development of advanced microsensors capable of measuring local strains and defects within FRP composites. One of the major challenges in this type of local structural health monitoring (SHM) is the development of embedded sensors which can be safely placed into FRP composites while maintaining their high strength and light weight. This paper presents the development and investigation of a set of wireless magnetostrictive sensors embedded within the FRP composites for in-situ and real-time monitoring of local strains inside the composites. Each sensor is a scalable thin film with a current thickness of 15 μm. Glass fiber reinforced polymer (GFRP) composite are investigated. The composite samples are made from 0° glass fiber fabric in an epoxy based resin. The sensors are embedded between the plies of FRP composites. Quasistatic tensile loading is applied to the sensor embedded FRP composites. An excitation planar coil placed close to the surface of the composites is used to send actuation signals to the sensors. During the loading on the composites, the local strains at the sensor increase, causing the magnetic flux change, which is measured by a pick-up planar coil located near the surface of the composite. The pick-up planar coil is connected to an external data acquisition system. This wireless excitation and sensing feature avoids any wire connections onto each sensor, which helps to maintain the light weight of the FRP composites, especially in large-sensor-array configurations. It has been found that the sensors have a nearly linear dependence between the sensor amplitude and the applied strain in the low strain region. At low strains, the strain sensitivity of the embedded sensors ranges from 4.12 μV/με to 10.30 μV/με. At higher strains, these sensors show a nonlinear response to the applied strains. The results indicate that these embedded magnetostrictive wireless sensors developed here are able to detect strains in situ and in real time within FRP composites while maintaining the light weight and high strength of these composites.
Soft Material Actuation and Shape-Change Through Ultrasonic Atomization
Remotely operated vehicles (ROV) conduct tasks that are generally dangerous or ineffective for humans, such as exploring and surveying the ocean for extended periods of time. Current underwater ROVs are complex assemblies of mostly rigid components designed for interacting with the environment, locomotion, and buoyancy control. Several recent studies introduced methods to replace these components with soft actuators to improve their performance. This study shows that actuation through ultrasonic atomization is capable of inflating a soft structure that can potentially replace traditional variable buoyancy systems. Ultrasonic waves were applied to a soft hollow structure that was partially filled with ethanol. The ultrasonic waves propagated through the wall of the structure and atomized ethanol into small vapor droplets. The liquid-togas phase transformation of the small droplets inflated the structure and controlled the buoyancy of the system. In this study, the volumetric change of the soft structure was analyzed underwater, and a simple system to control buoyancy was demonstrated
Planar Array Capacitive Imaging for Characterizing Subsurface Composite Damage
Despite the advantages and increasing prevalence of composite use in aerospace structures, they are susceptible to unique damage modes such as cracks, delamination, and manufacturing defects, which are fundamentally very different than those that occur in metals. In particular, subsurface damage can be hidden from plain sight and lead to catastrophic structural failure if left undetected. Thus, this study aims to develop a noncontact, nondestructive inspection technique (NDI) for rapid and in situ subsurface damage assessment of composites. The proposed technique works on the principle of electrical capacitance tomography (ECT). Unlike conventional ECT systems, electrodes were designed and arranged to form a planar array. By propagating electric fields in the volumetric region above the electrode plane and measuring mutual capacitances between electrodes, the volumetric permittivity distribution of the interrogated region could be determined. First, a custom electrode array was built, and different objects were interrogated for validation. The changes in electrical permittivity distributions (and their corresponding locations) due to the presence of these objects were successfully detected. Second, subsurface damage, such as voids, were introduced in 3D-printed test specimens, which were also successfully characterized by the planar ECT system. Last, a carbon fiber-reinforced polymer composite (CFRP) specimen was fabricated and then subjected to impact to introduce subsurface delamination. The specimen was imaged using planar ECT, and the reconstructed permittivity distribution was compared with Cscan images. The results showed that the proposed system was able to detect the location and extent of impact-induced delamination in CFRP panels, thereby demonstrating its potential as a viable NDI technique.
Fabrication Tolerance Study of Cold-formed Steel Members Based on Geometric Point Clouds of Laser Sensor
Cold-formed steel (CFS) industry grows fast in the last decade where more and more structures were constructed using CFSs as major load-bearing members. CFS is known for its light weight and high strength. As the material is minimized, the CFS members are inevitably sensitive to dimension variations so as to the cross-section properties, cross-section stability modes, and the resulting strength. The objective of this paper is to explore characteristics of dimension variations of typical cold-formed steel members, geometric point clouds of which were taken from high-throughput laser sensor platform. Variations of dimensions along the length of a specimen are displayed where closeness relationship between adjacent dimensions are observed. Thus, the correlations among dimensions of measured CFS members are extracted based on the reconstructed true geometries. Moreover, the comparisons between manual measurements and laser measurements have been made. The results may provide reference to manufacturing tolerance which were determined from manual measurements decades ago and may contribute to significant financial impact for the manufacturers. Simulations are applied to dimensions of models based on statistic parameters extracted from the measurement data of laser sensor. Various geometric properties are generated and fabrication factors are concluded corresponding to different properties. It is expected that the study may provide better understanding of dimensional tolerance and their impact on fabrication factors, thus the structural performance, which in turn may affect decisions of fabrication tolerance for coldformed steel industry.
An IoT Enabled Framework to Support Structural Health Monitoring Applications
Structural Health Monitoring (SHM) is a vital tool to improve the safety and maintainability of critical structures such as bridges and buildings. SHM is an approach to continuously collect data about critical structural elements using sensors to monitor and assess the condition of the structure; the sensory data includes loadings, stresses, strains, accelerations, temperature, and video signal, etc. Hence, it provides real-time and accurate information about the structural health condition. Wireless Structural Health Monitoring (WLSHM) is a promising solution to alleviate the cost and labor associated with the cabled monitoring system. Most WLSHM deployment scalability is constrained by the type of platform used for an existing deployment. These singlepurpose platforms are costly to covert for any new task or functionality in a system. Towards this end, to overcome the challenges listed, the paper proposes an open source IoT based framework to support WLSHM. The proposed framework enables ubiquitous services and powerful processing of sensing data streams beyond the capability of the traditional SHM system. The framework requires little or no domain knowledge for its deployment and operations and supports cheap, yet effective commercial-off-the-shelf (COTS) consumer products and open source technologies including Arduino boards and Raspberry Pis.
Using Intelligent Software Agents to Monitor the Structural Health of Highway Bridges
In Structural Health Monitoring (SHM) sensors can provide rich set of data on a bridge health condition. Wireless Sensor Network (WSN) is a promising paradigm that allow devices to coordinate and collaborate to effectively measure the health of a structure, whether the goal is to improve spatial resolution, structural resilience, bridge traffic, or perform advanced in-situ analysis. The WSN are generally equipped with a limited power supply, in most cases AAA batteries. Prolonging their operational lifetime is of fundamental concern. Even under the most stringent power management, the sensor nodes have an unattended life of approximately a few months to a year. The sensor can be mounted on very hard to reach areas on the structure, hence it is very difficult and in cases impossible to replenish their batteries. Towards this end, the paper proposes a multi-agent based system to augment the life time of WSN supporting SHMinformed ITS systems. The proposed agent-based approach (AA) consists of a mobile agent (MA) that can traverse the network, i.e., sensor nodes, mounted onto a bridge using multi-hop communication; collecting and aggregate the data, thus eliminating the two major causes of power consumption. These include (a) the direct transmission/broadcast from each sensor node to the sink and (b) redundant sensory data.
Control of the Building Parameters of Additively Manufactured Polymer Parts for More Effective Implementation of Structural Health Monitoring (SHM) Methods
Electromechanical impedance (EMI) method is one of the most widely used structural health monitoring (SHM) methods. The low-cost alternative of the method, surface response to excitation (SuRE) method has the similar characteristics. Both methods require additional disks/sensors to estimate the location of defects or external loading. Additive manufacturing (AM) methods have been matured and started to be an alternative to the conventional manufacturing. Most of the parts made by the AM methods are hollow. The infill type and skin thickness are selected to manufacture the parts faster by using less material and to avoid warping of the part. In this paper, manufacture of the parts with two infill types and two surface skin thicknesses at their different locations are proposed to estimate the site of the applied external forces without using additional disks/sensors. SuRE method was used in the study. The experimental results indicated that the frequencies of the most changed spectrum sections were different depending on the infill types and surface skin thicknesses. The same approach could be used for the EMI applications.
Non-Destructive Testing for Damage Detection in Composite Materials
One of the trends in the automotive, aerospace and energy industries is the use of lightweight structures (e.g. 3D printed structures, composite materials) in order to reduce the weight and improve the system performance. These industries have the need to estimate the presence of defects (e.g. Flat Bottom Holes (FBH) or Delamination in case of composite) both in-service and at the end of the production line. This work tries to find a solution to these problems. Two types of materials have been analyzed: Carbon Fiber Reinforced Plate (CFRP) and Glass Plate (GP). Due to the low weight of the structure it is not a good option to perform the measurements with classical sensors such as accelerometers and the tests have been performed in a less invasive way by using non-contact measurements. In the specific case, the OptoMET scanning Laser Doppler Vibrometry (LDV) measures the surface velocities and piezo-patch sensors (PZTs) excite the specimens in a very large frequency band (up to 40kHz). The algorithm for identifying the defects is based on the Local Defect Resonance (LDR) concept, which looks to the high frequency vibrations to get a localized resonant activation of the defect. To do that, an automated procedure able to analyze both time data with a time-frequency analysis and frequency data through modal analysis have been developed. The techniques have been validated in a lab environment and afterwards on real industrial components. Several defects with different sizes and locations have been identified. All techniques work also if more defects are located within the analyzed region. Future steps foresee the use of innovative modal parameter estimation techniques in the loop and the use of machine learning algorithms.
Towards On-Chip Measurement of S-Parameters for Ultrasonic Guided-Wave SHM: Damage Localization in Aluminum Using S-Parameter Measurements
This paper investigates miniaturized hardware for active SHM based on ultrasonic guided-waves. The proposed approach relies on frequency-domain S-parameter measurements instead of time-domain pulsing and thereby trades off longer measurement times with lower actuation voltages for improved compatibility with dense CMOS chip integration. To demonstrate the feasibility of this approach, we show defect localization measurements for an aluminum plate using a benchtop vector network analyzer using 0 dBm (0.632 V ) actuation signals. The challenge of designing directional couplers in the 10 kHz – 10 MHz target frequency range of the system is discussed and ongoing work to address this challenge is presented.
Nonlinear Component Extraction Using Singular Value Decomposition and Its Application to Breathing Crack Localization
The identification of the spatial location of breathing crack is a highly challenging problem as the change in the dynamic characteristics of a structure with a breathing crack is much smaller when compared to their counterpart open crack. Majority of the breathing crack localization techniques reported in the literature are based on the comparison of the amplitudes of the first few higher order harmonics obtained spatially across the structure under harmonic excitation. The amplitude of superharmonics is of the lower order of magnitude than that of linear or excitation harmonic component and often gets buried in the noise components as both have the same level of energy. The present work attempts to identify the amplitude of all the superharmonics directly from the singular values of the acceleration time history response using singular value decomposition. Numerical and experimental studies have been carried out to validate the singular value decomposition based breathing crack localization technique. Investigations concluded that the proposed technique is capable of localizing single and multiple breathing cracks in the structure.
UAS-Based 3D Reconstruction Imagery Error Analysis
With the rapid development of navigation, guidance and flight control systems, and sensing technologies, lately the market of small Aerial Systems (UASs) has boomed. UASs have been deployed in many different fields, including inspection of civil infrastructure conditions. To monitor structural performance, a UAS will be equipped with different sensing devices based on the task and desired data product. So far, the two most commonly used sensors on UAS are optical cameras and Light Detection and Ranging (LIDAR) systems. By processing images of structures captured from a hovering UAS, a better understanding of structural conditions such as structural defects sizes or locations, or structural displacement may be achieved. For residential or commercial buildings, people often use 3D models reconstructed from multiple 2D images to analyze and assess structural conditions. The 3D models obtained directly from 2D images do not have an absolute scale, and they are georeferenced via integration with ground control points or with additional sensors, such as LIDAR system. The quality of structure assessment is highly dependent on the accuracy of the 3D reconstruction and geo-referencing. However, various types of issues can arise in UAS-based 3D reconstruction, for instance, from the real-time image collection process, image and feature processing, model estimation, and integration with sensors or control points. In this paper, we will identify the major error sources, then quantify or simulate these errors, and estimate their contributions to the overall error in the structure assessment process. A set of imageries of regular shape models captured from the LIDAR onboard a small UAS will be collected and analyzed. These representative images will be used as examples to visualize and quantify the error sources of structural monitoring. Based on the error analysis results, we will discuss approaches to potentially constrain the error sources and mitigate their impact on the assessment process.
Spatial Resolution Improvement of Digital and Thermographic Imaging Using an Automatic and Controllable Super-resolution Technique
Infrared thermography has become a widely applied Non-Destructive Testing (NDT) approach to detect near-surface and sub-surface defects of various materials. The detection accuracy and reliability is largely limited by a relatively low spatial resolution of thermal cameras in comparison to visible-light cameras. Using a high-end thermal camera to achieve high spatial resolution can be very costly. Furthermore, the maximal spatial resolution may be unachievable when a high sample rate has to be used for high heat-conductivity materials, such as metal. This paper reports an automatic and controllable spatial resolution improvement technology to improve the spatial resolution of passive thermal images without replacing the existing sensors. A high-resolution image is reconstructed by fusing a sequence of low-resolution images captured by precisely controlling the position between specimen and camera. The performance of the proposed system has been tested on both digital and IR cameras. Both visual and quantitative results demonstrate the considerable improvement of image quality.
Event-Based Phased Array Signal Processing
In the last few years video-based techniques for identifying the dynamic properties of structures have become of increasing interest. These techniques offer the possibility of capturing high-resolution structural dynamics information at very low cost. In recent years event-based imaging technology has also become commercially available. Event-based imagers differ from conventional imagers in that they only report data for pixels whose intensity value has changed. The result is that these imagers can capture high-speed phenomena with relatively low data rates. The data rate of the imager essentially automatically adapts to the speed at which event that it observes are occurring. As a result the eventbased imager is very efficient. In this work we begin exploring the possibility of adapting techniques from the field of phased-array signal processing to the data captured from event-based imagers. In this case we consider each pixel of the event-based imager to potentially be a sensor in the array. The goal of this work is to eventually develop high-speed, event-based image processing algorithms that can be used to help monitor structural integrity. These techniques could also be applied to emerging types of eventbased signal processing sensors such as ultrasonic sensors that could be used to enable low-cost, low-power ultrasonic phased arrays.
MIMO Radar-based Real-Time Structural Health Monitoring
The paper proposes an innovative methodology for real-time structural health monitoring and geophysical applications. The key elements of the system are a high performances MIMO RADAR sensor, an optical camera and a dedicated set of software algorithms encompassing interferometry, tomography and photogrammetry. The MIMO RADAR sensor proposed in this work, provides an extremely high sensitivity to displacements making the system able to react to tiny deformations (up to tens of microns) with a time scale which spans from milliseconds to hours. The MIMO feature of the system makes the system capable of providing a set of twodimensional images of the observed scene, each mapped on the azimuth-range directions with noticeably resolution in both the dimensions and with an outstanding repetition rate. The back-scattered energy, which is distributed in the 3D space, is projected on a 2D plane, where each pixel has as coordinates the Line-Of-Sight distance and the cross-range azimuthal angle. At the same time, the high performing processing unit allows to sense the observed scene with remarkable refresh periods (up to milliseconds) thus opening the way for combined static and dynamic structural health monitoring. Thanks to the smart TX/RX antenna array layout, the MIMO data can be processed through a tomographic approach to reconstruct the three-dimensional map of the observed scene. This 3D point cloud is then accurately mapped on a 2D digital optical image through photogrammetric techniques, allowing for easy and straightforward interpretations of the measurements. Once the three-dimensional image is reconstructed, a 'repeat-pass' interferometric approach is exploited to provide the user of the system with high frequency threedimensional motion/vibration estimation of each point of the reconstructed image. At this stage the methodology leverages consolidated atmospheric corrections algorithms to provide reliable displacement and vibration measurements.
Study of the Damage Detection Approaches for Additively Manufactured Parts
The additive manufacturing (AM) is a new production technique that can change the approach to design and manufacture of future structures. One of the synonyms of AM is 3D printing. It is now used not only for making prototypes but for real parts. More and more structural parts are being manufactured using AM and there is a need to study the structural health monitoring approaches for such parts. In the reported research the focus is put on non-contact techniques. The guided waves approach is studied as well as terahertz spectroscopy. The guided waves were excited by piezoelectric sensor and the surface response was gathered using the scanning laser vibrometer measurements. The terahertz time domain measurements were conducted in the reflection mode in which access to only one object surface is needed. The inspected object was prepared with simulated damage in the form of voids inside the material. The characteristic feature of AM is layer by layer production so depending on the designed the manufactured part can have different properties if the layers are laid in one or other direction. In this research the influence of this directionality on the considered methods is studied. Damage detection approaches are developed taking into account the properties of the printed materials. The gathered time signals are processed with the developed signal processing algorithms.
Magnetorheological Fluid Device for Semi-Active Control of Hydraulic Damping
The use of MR fluid offers a simple and direct control of the resistance force. Moreover, low energy consumption can be assured by battery and does not represent a danger for user. In the literature there are presented examples of the use of MR fluids for vibration damping but these examples aim at replacing the hydraulic fluid in the shock absorber with a MR fluid. The solution is viable but expensive compared to the price of passive type dampers (but cheaper than active damping) due to the use of a large volume of MR fluid. Another disadvantage of the solution presented above is the greater weight of the MR fluid device compared to the same hydraulic fluid device (the MR fluid density is much higher than that of the hydraulic fluid). Therefore, the aim of this paper is to presented a magnetorheological fluid device designed to control the damping force developed by a hydraulic shock absorber (magnetorheological piloted hydraulic damper). This type of damper (magnetorheological piloted hydraulic damper) has several advantages as: reduced cost due to the small quantity of magnetorheological fluid required, reduced weight compared to classical MR fluid dampers, aspect and overall dimensions similar to those of the passive damper and maintains the benefits of classical MR fluid dampers. Because the metal particles in the MR fluids tend to sediment, it is necessary to overcome the density of the fluid in applications where the staging time is high (seismic protection dampers). This monitoring is done with a PWAS device.
Structural Damage Classification Using Machine Learning Algorithms and Performance Measures
The use of data-driven algorithms for Structural Health Monitoring (SHM) allows continuous monitoring and online damage identification in structures subjected to changes in its operational and environmental conditions along its lifetime. The principal goal for SHM is oriented to the development of efficient methodologies to process the data obtained directly from the structures under inspection and provide results associated with the different levels of the damage identification process. Some advantages in the use of data from the structure are for instance that data are obtained from a sensor network which is permanently installed to the structure allowing to know in any time the state of the structure, it is possible to identify different kind of damages and the possibility of enhancing the information about the damage size, position, among others. However, one of the challenges in SHM is the development of algorithms or methodologies with an accuracy that allows to avoid false damage identification. As a contribution, this work presents a damage detection and classification methodology which use multivariate analysis and machine learning algorithms as a pattern recognition point of view. The validation of the methodology is supported by some performance measures that provide relevant information about the classification behaviour to select the different elements in the methodology. The methodology is tested with data from a structure with a piezoelectric sensor network and the results show that it is possible to demonstrate its usefulness in the damage detection and classification process.
Intelligent Structures Using Textile Reinforced Concrete– Achievements and Challenges
This study presents the concept of a carbon based-sensory textile reinforced concrete (TRC) element that combines an advanced material/structural system with sensing capabilities. The concept is based on the use of glass and carbon fiber based textile for the reinforcement of thin-walled concrete elements and, at the same time, to use the electro-mechanical properties of the carbon components as a sensory device. This combination aims to provide the structure with the structural and sensory features required for an intelligent load bearing system. The research presents the applicability of the concept to crack detection and damage quantification, and to detection of water infiltration. The study presents the feasibility of the hybrid TRC structures and reviews the challenges that should be faced in order to bring the technology into realization in the future.
The Israeli Air Force Efforts in Implementing Fiber-Optic Nervous Systems in Composite Based Air Vehicles for Damage Estimation and Performance Analysis
Composite structures lack retirement time assessments and their operational integrity highly depends on the manufacturing process and operational history. Furthermore, due to their material diversity and variability among structures, in many cases their integrity cannot be easily examined by standard non-destructive inspections. For these reasons, the Israeli Air Force (IAF) has been adopting and developing global structural integrity and health monitoring methodologies based on fiber-optic point and distributed sensing technologies, aiming at providing early warnings before failure, smarter utilization and condition-based maintenance for best availability of IAF Unmanned Air Vehicle (UAV) fleets. As of today, the Fiber Optic Sensing (FOS) infrastructure is recording real-time measurements of strain, temperature and vibrations in operational flights. This paper describes the IAF efforts in implementing fiber-optic nervous systems in composite-based air vehicles, including the use of damage detection algorithms.
Highway to SHM: Elbit’s Fast Implementation of an On-board Monitoring System for Flying Platforms
This paper describes the relatively fast (< 3 years) execution of Elbit’s decision to adopt and implement (practically from scratch) an on-board fiberoptic- based Structural Health Monitoring (SHM) system on a large Unmanned Aerial Vehicle (UAV), mainly for in-flight load monitoring. The paper presents the developmental process, which started from a state of no experience with optical fiber-based monitoring systems, and ended in a fully operational and validated airworthy UAV, culminating with the first implementation of this technology in a multi-plane project. The paper portrays the careful shift from a Research and Development (R&D) phase, dealing with coupons, to a full-scale flying airplane, while meeting all managerial and engineering requirements and concerns.
Experimental Tracking of Helicopter Rotor Blade Hidden Crack Development Using Fiber-Optic-Based Sensing Technology
An optical-fiber-based SHM concept for the detection of a hidden damage in a helicopter rotor blade was proposed, and demonstrated in a cyclic loading test. Not only could the sensing network detect the hidden cracks in the rotor root structure, it could also follow their growth at a stage where the damage was too small to compromise the blade ability to sustain the test cyclic loading.
Choosing the Right Optical Fiber Coating to Meet Individual Fiber-Optic Strain Measurement Application
Accurate optical fiber-based strain measurements in Structural Health Monitoring applications critically depend on proper choice of the fiber coating, which transfers the substrate strains to the fiber core. In this paper, the load-transfer properties of several commercial fiber coatings were experimentally characterized and a transfer gauge length was defined based on an analytical model. The shorter the transfer gauge length, the more accurate is the measurement of complex strain gradients. The coatings evaluated are: standard double-layer acrylate (longest gauge length), ORMOCER® by FBGS, single layer hard acrylate by OFS, and standard polyimide coating (shortest). These gauge length values may help the selection of the proper fiber coating for a specific application, taking into account the actual strain gradients to be measured. Besides, the proposed measurement technique, together with the developed analytical tool, can be used for the characterization of other fiber coatings.
Calibration of a Discrete and Distributed Fiber-Optic-Based Strain-Measuring Network for the Shape-Sensing of a Wing Model Under Dynamic Loading of a Wind Tunnel
Fiber-optic-based, real time, dynamic discrete and distributed strain-based shape sensing techniques were applied to a specially designed, rapid prototype wing (made of ULTEM-9085), preparing it for wind tunnel testing. This work describes the calibration methodology of the sensing network, including the accurate spatial mapping of the sensing points with respect to the structure, as well as the network responses to prescribed bending, torsion and impact loading. Much less intrusive than electrical strain gauges, the calibrated fiber-optic sensing network successfully provided real-time tracking of the wing aeroelastic performance in wind tunnel experiments, which culminating in wing flutter.
Filtering Analysis of Acoustic Emission Parameters for Damage Fracture Characterization of Reinforced Concrete Beam Subjected to Four-point Bending
Acoustic emission (AE) technique was used to characterize the damage fracture process of reinforced concrete (RC) beam subjected to four-point bending. Mechanical performances and AE parameters were recorded through laboratory experiment. Gaussian filtering was adopted to interpret the AE energy and b-value obtained during testes to characterize the crack intensity and behavior. The results reveal that Gaussian filtered AE energy can realize the identification of the damage fracture process, and five stages can be classified. As for Gaussian filtered b-value, crack behavior identification effect of b-values calculated from all sensors is unsatisfactory. However, Gaussian filtered b-values obtained from amplitudes recorded through sensors at critical damage regions present clear trend and favorable identification effect.
Lamb Wave-based Depth Characterization of Acoustic Emission Sources in Complex Metallic Plate-like Structures
This paper demonstrates an inversion algorithm for characterizing the depth of acoustic emission (AE) sources in complex metallic plate-like structures. The characterization is motivated by gaining insight into early-stage fatigue crack propagation in such structures. The primary inputs to the algorithm are narrowband-filtered AE signals from a network of sensors and an estimate of the planar source location. The layout of the sensor network is specially designed to make use of locally isotropic regions in a complex structure. The algorithm is based on automatically extracting the depth-dependent amplitude ratio between the fundamental Lamb modes contained in the AE signals. A finite element model is designed to solve the forward problem of Lamb wave motion due to a given source, from which the relation between source depth and amplitude ratio is determined. The proposed algorithm was evaluated on a 1 m × 1 m section of a 2025-T6 aluminum civilian aircraft fuselage. Validation of the algorithm was achieved by generating controlled AE sources (Hsu-Nielsen pencil lead breaks) at various depths along the edges of the fuselage panel. Good agreement was found in the between the true and estimated source depths. The experiments demonstrate the ability to distinguish between sources originating near the mid-plane of a plate-like structure from those near the surface. Moreover, it is demonstrated that the fast computation of the inversion algorithm has great potential for real-time monitoring applications.
Clustering Acoustic Emission Time-series Using Unsupervised-shapelets
The sensitivity of AE sensors makes the AE technique very interesting for detecting damages at various scales as encountered in complex heterogeneous materials made of various constituents with different damage kinetics. However, there are unknowns behind the generation process of AE signals, as well as behind the modification of those signals along the propagation path until the sensors. Those make the interpretation of AE signals a difficult task in terms of pattern recognition. Even though some damage families are expected to occur for a given material under specific loading, there is a lack of knowledge to bridge the gap between AE signals collected on sensors with the related AE source. This accounts for the use of unsupervised learning when one is interested to discover relationships between AE data. When using clustering for unsupervised learning, the output is a set of clusters representing a set of labels assigned to each datum and forming a partition. In AE work, clusters are estimated using features, computed from AE signals. Feature extraction has become a must-to-do step due to the use of particular clustering methods and to the fact that AE streaming has not been particularly analysed for damage sources identification. The contribution of this paper is a new methodology for AE signal clustering which does not require feature extraction as usually done. It works directly on raw AE signals derives from several tools of the literature, namely unsupervised shapelets, anomaly detection and statistical modelling by Autoregressive Weakly Hidden Markov Models. When used in inference on unknown signals or streaming, those models allow to generate a set of novelty scores which are then processed by a chronology-based clustering algorithm to get a partition, accompanied by the uncertainty around clusters and a quantification of the robustness of the results obtained.
Integration of Distributed Optical Fibres and Piezoelectric Sensors for SHM of Composite Tubes
The aim of the study is to detect, localise, and attempt to classify damage mechanisms in a carbon fibre/epoxy composite tube from an early stage of loading until final failure. Parametric analysis is completed on acoustic emission (AE) data to attempt damage classification, while experimental wave velocities are used to estimate damage locations. This is complemented by damage index calculations based on the amplitude reduction observed in guided wave signals. The application of an unsupervised clustering algorithm enables observation of the evolution of damage, as separated into groups. When overlaid with the cumulated energy obtained from AE signals, these “damage profilesâ€ for each cluster of data can be linked more closely to the known damage processes.
Structural Health Monitoring of Adhesively-Bonded Hybrid Joints by Acoustic Emission
The increasing use of Adhesively-bonded joints in industrial applications resulted in more attention to damage assessment in these joints. The aim of the present study is to characterize the damage in bi-material double-lap adhesively-bonded joints by Acoustic Emission (AE). Two different structural adhesives, representing a ductile (Methacrylate-based) and brittle (epoxy-based) types, were used to bond CFRP skins to a steel core. The fabricated joints were loaded in tension while damage evolution was monitored by AE. Due to the difference in the fracture nature of the adhesives “brittle vs. ductileâ€, different damage mechanisms occurred in the specimens; including adhesive layer failure, steel deformation, adhesive/adherends interfacial debonding and delamination in the CFRP skin. In order to distinguish and classify these damage types by AE, the AE features of each damage mechanism were first obtained by conducting some standard tests on the individual constituent materials. Then, these AE reference patterns were used to train an ensemble decision tree classifier. Finally, the trained model classified the AE signals of the double-lap tests and the images captured by camera were utilized to verify AE results. This study demonstrates the potential of AE technique for damage characterization of the adhesively-bonded bi-material joints.
Quantifying the Benefit of Structural Health Monitoring: Can the Value of Information be Negative?
Structural Health Monitoring (SHM) is generally presented as a powerful tool that allows bridge managers to make decisions on maintenance, reconstruction and repair of their assets. The benefit of SHM can be properly quantified using the concept of Value of Information (VoI), i.e. the difference between the utilities of operating the structure with and without the monitoring system. In calculating the VoI, a commonly understood assumption is that all decisions concerning system installation and operation are taken by the same rational agent. Conversely, in the real world the individual who decides on buying a monitoring system, the owner, is often not the same individual, the manager, who will use it, and they may behave differently because of their different risk aversion. In this contribution, we demonstrate that in a decision-making process where the two individuals involved share exactly the same information, but behave differently, the VoI can be negative. Indeed, even if the two agents have an agreement a priori, their optimal actions can diverge after the installation of the monitoring system, due to their different behaviors; this scenario could generate a negative VoI from the owner’s perspective. We propose a qualitative and quantitative formulation to evaluate when and under which circumstances the VoI can be negative, if the owner differs from the manager with respect to their risk prioritization. To illustrate how this framework works, we evaluate a hypothetical VoI for the Streicker Bridge, a pedestrian bridge in Princeton University campus equipped with a fiber optic sensing system, assuming that two fictional characters are involved: Malcolm the manager and Ophelia the owner. The results demonstrate that when the owner, because of the manager’s different behaviour, is forced to undertake an action she would not choose, her VoI becomes negative, i.e. it is not convenient for her to install the monitoring system. This framework aims to help the owner in quantifying the money saved by entrusting the evaluation of the state of the structure to the monitoring system, even if the manager’s behavior toward risk is different from the owner’s own, and so are his management decisions.
Optimal Ultrasonic Sensor Configuration for Plate-Like Structures Using the Value of Information
Condition-based maintenance is based on reliable and effective structural health monitoring systems able to provide accurate information about the health condition of a structure. Among the factors that influence the accuracy and reliability of such sys- tems, the number and position of sensors stand out as key features. As a rule, the larger the number of sensors, the more and better information can be obtained. However, a practical solution is required to find a healthy balance between the cost of the monitoring system and the adoption of the information that they provide as optimality. To rigorously address such optimization problem and provide an optimal design of the monitoring system, this paper proposes the use of the value of information and the expected information gain. The methodology is illustrated using a stiffened plate-like structure with a bounded damaged area, ultrasonic guided-waves as SHM technique, and different sensor positioning strategies. The results reveal the value of information as a rational index to provide optimal ultrasonic sensor configuration and rank different positioning alternatives.
Optimal Decision Making for Cost-effective SHM Planning
This paper investigates the optimal decision making for cost-effective structural health monitoring (SHM) planning. The uncertainty of the monitoring data is represented by the availability. The prediction error between the actual value of parameter and monitored parameter is considered in the formulation of the availability of monitoring data. Based on the availability of monitoring data with the loss estimation, the decision making is applied to find the best alternative associated with SHM planning. In this decision making, the expected monetary values (EMV) of all the possible alternatives are computed and compared. As a result, the alternative with the maximum EMV leads to the best cost-effective SHM plan, which provides the optimum monitoring and prediction durations. Furthermore, in order to integrate the uncertainty associated with the damage occurrence and propagation into the decision making for optimum SHM planning, the expected damage detection time and delay are estimated. The optimum SHM plan is obtained by solving the optimization problem to minimize the expected damage detection time and delay. Finally, SHM starting times are optimized.
Optimal Maintenance Decision for Deteriorating Components in Miter Gates Using Markov Chain Prediction Model
Maintenance of miter gates in navigation locks is crucial to facilitate water navigation of cargo ships. Scheduled and unscheduled maintenance are the two types of actions performed to keep miter gates continuously operational. In practice, visual inspections and rating systems are used to monitor the current state of miter gate components in order to prioritize maintenance. This paper shows an example of how to find the optimal time to plan for maintenance using deterioration curves obtained from real visual inspection assessments of components in miter gates. Predictive deterioration curves are derived using a Markov Chain approach. In addition, the advantages of implementing Structural Health Monitoring (SHM) are discussed.
Decision Making Based on the Value of Information of Different Inspection Methods
Decision makers face several challenges in the aftermath of a disastrous event. Emergency operations such as traffic restrictions, bridge closures and immediate repairs might have to be scheduled in order to keep an appropriate level of safety. The state of the infrastructure after the occurrence of the seismic event can be predicted based on the a priori knowledge about the system, and decisions may be taken based on this knowledge. This is generally done by minimizing the expected cost of the different plans of action, considering both direct (e.g. failure of a component) and indirect costs (e.g. costs coming from diverted traffic after a bridge closure). However, inspections of selected critical components can provide insight into the actual state of the system and consequently change the chosen plan of action for the decision makers. Different methods of inspections come at different costs, information and uncertainties. It is important to decide whether or not inspections should be done and if so select the most appropriate inspection method so that the associated costs do not overcome the benefits that would be obtained by performing it. Moreover, advanced monitoring techniques that are able to investigate the state of the system over time (such as sensor monitoring) can provide additional information about the way the system is evolving. This information can in turn be used to update the knowledge about the state of the system before the occurrence of a disastrous event, further reducing the chance of taking the wrong managing decision. Before installation, decision makers must properly evaluate the costs associated to these monitoring techniques, to decide whether the additional information overcomes their cost. In this work, we propose a general framework to evaluate the Value of Information of selected inspection procedures considering the possible information from structural health monitoring (SHM) of system. The proposed framework will be applied to a case study of a two-span reinforced concrete bridge. 1
Measurement of Individual Risk Preference for Decision-Making in SHM
Effective decision-making methods have a large importance for sustainable management of infrastructure. Modern decision-making processes are increasingly data-based and sensor technologies enable data-based decision-making processes to be implemented in infrastructure management. In all decision-making, data-based or not, risk preferences of decision maker are a key consideration, and can explain why even rationally acting individuals could end up making different decisions under the same circumstances. Previous work in the field has shown that when using expected utility framework in a multistakeholder setting, differences in stakeholder risk preferences have large impacts on the optimal decisions. A key result in literature establishes the importance of risk-attitude measurement in infrastructure manager selection by showing that large discrepancies in risk attitudes can create a situation where a SHM system has a negative monetary value. As a first step in developing better decision procedures we have created a risk attitude measurement scale for infrastructure management purposes. The scale is derived from a widely utilized Domain-Specific Risk-Taking (DOSPERT) Scale, which utilizes a riskreturn framework where risk taking is decomposed using perceived risks and benefits. We conducted a pre-test of the scale with a select student population. The pre-test results are encouraging in terms of quality of our scale and also give early evidence that SHM decision-making differs from ordinary maintenance decision-making.
Integrated Life-cycle Decision Framework for Structural Systems
A large part of the investment in public infrastructure is dedicated to designing and constructing the infrastructure and to mitigating deterioration processes, such as corrosion and fatigue damage. In practice, the different phases of a structure’s life-cycle are managed by different teams with little interaction. Unfortunately, the optimization of the individual phases does not necessarily result in an optimal life-cycle cost. This motivates the development of models and procedures that treat structural optimization from a broader life-cycle perspective. This paper discusses the possibility to enhance the design of structural systems by reflecting the operation and maintenance phase in the design decision framework. The inter-dependency between decisions made at different points during the life-cycle is accounted for by means of a sequential decision analysis. A hierarchical influence diagram is proposed as an efficient way to model the probabilistic decision framework while considering system effects, such as the spatial correlation of the deterioration mechanisms. Challenges in the implementation of the framework are documented and potential applications are discussed. A simple example is presented to assess the performance of the model.
Quantification of the Posterior Utilities of SHM Campaigns on an Orthotropic Steel Bridge Deck
This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management.
A Co-Word Analysis of the Structural Health Monitoring Field
This research applies a bibliometric analysis to the structural health monitoring (SHM) research field over a 15-year period, from 2003 to 2017. The goal of this research is to provide a broader analysis of the field as a whole through a data-driven approach with a bibliometric analysis of SHM literature. The aim of this work is to understand relationships between research themes and communities within the SHM field, and how this landscape has evolved over time. By employing graph theory and social network analysis methods, this study uses a data-driven analysis using a co-word and co-venue analysis based on publication data from over 21,000 conference and journal papers in SHM. Strategic diagrams and network analysis were used to map and visualize the academic landscape of SHM. Additionally, a co-venue analysis was performed to determine the conference and journal research communities within the field at each time segment. The analysis was conducted for three 5-year time periods providing insight on how the communities within the SHM research field have evolved.
Value of the SHM System in the Context of Risk-based Inspection Planning
Risk-based inspection (RBI) planning is concerned with the optimal allocation of deterioration control which is based on the pre-posterior analysis using the Bayesian decision theory and the expected utility theory. It has been widely used to make inspection and repair strategy in the field of civil engineering and infrastructure systems. Nowadays, with the developments of sensing and inspection technology, SHM has been widely adopted as an extended tool to provide decision support in the RBI planning. Structural health monitoring (SHM) systems can provide continuous performance information to decrease the existed uncertainty in engineering risk analysis. However, the quantification of benefits in the viewpoint of utility by SHM has not received widespread attention. The present paper is aimed to describe a framework of preposterior analysis for support of decisions in the viewpoint of life-cycle cost analysis (LCA), apply the concept of Value of Information (VoI) to make the optimal SHM strategy decision in the context of RBI planninng. The VoI concept is herein elaborated through an illustrative example involved the fatigue-induced degradation as well as two simplified approaches of RBI planning. It is demonstrated how VoI can be used to effectively provide a decision-making support of SHM strategy in the strategy level of RBI planning.
Method of Generating Economic Value from Navigation Lock Structural Health Information
Structural Health Information (SHI) can be collected from two different types of data sources. They are the CMMS (Computerized Maintenance Management System) and the historian systems. CMMS contains information about maintenance operations, such as work orders and spare parts stock. Historian contains real time and historical operational data, such as time series obtained from sensors. Collecting, identifying, accessing, cleaning, enhancing, fusing, and analyzing this data is a challenge for large organizations like the U.S. Army Corps of Engineers. Maintenance data do exist, but it may not be organized or accessed easily as they are managed by various entities of the organization. To develop a complete structural health monitoring (SHM) solution, both data sources should be used. This paper proposes a process for the maintenance data only, converting them into valuable information for the maintenance strategy, investment decisions and the optimization of asset’s lifecycle performance. Statistical methods and stochastic processes are utilized based on the data type (e.g. inspection, repair, replacement) and the overall process is demonstrated with an example from a river navigation lock.
Practical Considerations in Utilizing Structural Health Information in Bridge Maintenance
The paper presents two illustrative examples about the utilization of structural health information for rational decision making in everyday bridge management. It is suggested that Bayesian decision analysis could be useful even in sample cases to help efficiently use specific and generic information about structural condition. However, there are some challenges concerning practical application.
An Efficient Augmented Reality (AR) System for Enhanced Visual Inspection
While manual visual inspection of structures has the advantage of being relatively simple and low cost, it is usually time consuming, labor intensive and highly subjective. Augmented reality (AR), because of its ability to provide the user with additional information about the working environment in real-time, has been used in the past to address some of the limitations of manual visual inspection by supporting human workers during the inspection process. The paper presents the development of an efficient deep learning (DL) based augmented reality (AR) system for identifying critical departures from the pristine state of the structure with focus on two anomaly categories- corrosion and fatigue cracks. Most of the common AR devices usually come with a built-in camera for capturing image/video data, a storage and a microprocessor. However, due to the limited processing power, the underlying deep learning (DL) model has to be first trained externally and a suitable version of the trained model is then deployed locally on the device. The model then outputs information for identifying critical departures from the pristine state of the structure e.g., highlighting corroded regions, fatigue cracks and/or combination of both. This information is overlaid real time over the current field of view through either a headmounted or a hand-held AR device in order to augment the human vision. The worker can then focus on the highlighted region for a more detailed inspection. The feasibility of the proposed AR system is demonstrated using laboratory inspection of common mechanical components likes pipes, plates etc. In order to enable the model to keep learning based on the inputs from the AR glasses, a strategy for federated learning is introduced towards the end of the paper.
Dimension Reduction Algorithms in the Damage Indexes Space for Damage Size Quantification in Aeronautic Composite Structures
The Structural Health Monitoring (SHM) process is classically decomposed into four steps: damage detection, localization, classification and quantification. Here the focus is put on aeronautic composite structures and specifically on the damage quantification step. For SHM purpose, such structures are equipped with piezoelectric elements that can be used both as sensors and actuators. To quantify a detected damage, measurements are first performed in a reference state. Then, during the life cycle of the structure several measurements at unknown states are performed. Several damage indexes are then extracted from the difference between the reference and unknown states. This damage indexes matrix is the basis of any algorithms dedicated to the quantification step but still contains many more dimensions that just a quantification of damage size. The question raised here is the efficiency of dimension reduction algorithms in the damage indexes space for quantification purposes. Performances of simple direct regression (SDR), principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA) and autoencoders (AE) are investigated for this purpose. It is shown that PCA, PLS and CCA are all able to discover a low-dimensional space within the damage indexes space that is linearly related with the physical damage size, and that average prediction errors of the order of ≃ 1% can be achieved by projecting data through that low-dimensional space.
Structural Health Monitoring for Aerospace Composite Structures
Sensitivity characterization is a necessary process for the evaluation of a Structural Health Monitoring technology (SHM). It is needed to feed reliability assessment with respect to damage/flaw detection. To this end, we propose in this study the investigation of an SHM sensitivity issue for composite structures that have undergone impact events with different delamination area and size.
Damage Diagnosis and Prognosis in a Composite Structure by Surrogate Modelling and Particle Filtering
Bayesian model updating has proven to be successful for inverse problem solutions, such as for the evaluation of the posterior probability distribution of the parameters of interest, conditional on the observations of some damagedependent features. Specifically, the sequential Monte-Carlo procedure generates possible damage evolution trajectories based on available process models and then assigns each trajectory a weight according to its likelihood. However, for realistic structures, computationally expensive numerical simulations might be required for the evaluation of the trajectory likelihoods. In this work, the computational problem is addressed by leveraging on surrogate modeling, while particle filtering provides the sequential Bayesian framework for performing simultaneous diagnosis and prognosis of a composite double cantilever beam specimen subject to fatigue delamination growth, based on observations of the strain field pattern acquired at some specific locations. The algorithm is successfully tested with respect to a case study of a propagating delamination.
Upscaling the Data-driven Prognostic Methodologies Towards a Condition-based Structural Health Management of Composite Structures
We present a conceptual methodology built in the framework of the ongoing EU H2020 Real-time Condition-based Maintenance for Adaptive Aircraft Maintenance Planning project, that leverages structural health monitoring data from different sensing technologies that goes a step beyond damage detection and diagnosis towards the probabilistic remaining useful life estimation in the presence of adverse conditions during flight. The methodology relies in several parallel activities from damage detection and localization to damage identification and severity assessment, sensitiveto- damage feature extraction processes, training methodologies, data fusion and remaining useful life predictions. Various sensing technologies i.e. static and dynamic strain sensing with FBGs, guided waves and acoustic emission are employed. An extensive hierarchical test campaign on test articles of increased complexity based on the building block approach is discussed with details as to the types of damage that are going to be targeted. Single and multi-stringer composite stiffened panels are subjected to realistic loading conditions. Emphasis on impact damage and skin/stringer fatigue disbonding/delamination is given. Last but not least, sophisticated mathematical algorithms are proposed e.g., multi-state degradation models such as Non-Homogeneous Hidden Semi Markov Model in order to deal with the data-driven RUL prediction with uncertainty quantification.
Delamination Detection Using Electrical Resistance Change Method and its Reliability
Delamination detection on carbon fiber reinforced thermoplastic composites was investigated by application of the electrical resistance change method. Experimental data are compared to those obtained during delamination growth on carbon fiber reinforced polymer composites. Numerical study using the finite element method was also performed in order to investigate the influence of the change of the temperature, and nominal resistivity and of the distance of the electrical contacts on the relationship between the delamination length and the measured electrical resistance. Good agreement was found between the experimental results and the numerical investigations.
Composite Material Remaining Useful Life Estimation Using an IoT-Compatible Probabilistic Modeling Framework
Monitoring real-time failure in fiber-reinforced composites using nondestructive evaluation (NDE) is a fairly difficult task due to the associated evolving material state uncertainties, as well as due to data management mining and signal processing challenges. In this context, a number of statistical, probabilistic and physics-based models have been proposed to make predictions of remaining useful life (RUL) in the built environment. In parallel to modeling efforts, the data quality from sensing, NDE and testing methods is compromised by a variety of intrinsic (hardware) and extrinsic (noise) factors that make the damage assessment process both challenging to visualize and computationally expensive to analyze. Most importantly, the uncertainties in the recorded data, also hinder efforts to create data-driven methods in the framework of what is currently called digital twin modeling. To address such issues more efficient data workflows and processing procedures are currently sought to assist with both damage monitoring assessment, as well as modeling and predictions. The objective, therefore, of this manuscript is to present a novel datadriven probabilistic modeling methodology that is capable of producing RUL estimates for composites. The framework consists of two parts: the hardware/software integration that allows the establishment of data streaming and handling procedures that ultimately feed in real time the probabilistic approach. The proposed probabilistic modeling approach is based on the use of an outlier analysis combined with information quality metrics to identify a given set of degradation states. Predictions of RUL are then made by extracting and processing features from NDE datasets which are then used in unsupervised clustering. Such information is subsequently leveraged to train a support vector machine (SVM) alongside a hidden Markov chain model (HMM). The signal classifications from SVM combined with the HMM are then used as inputs for an adaptive neuro-fuzzy network that produces the RUL predictions. An application of the proposed approach in mechanical testing experiments of an aerospace-grade composite material is presented. Extensions that can make this approach applicable to industrial applications as well as in digital twin implementations are discussed.
Influence of Bending Loads on Ultrasonic Guided-Waves Based Structural Health Monitoring of Laminated Composite Structures
In this paper, an investigation is carried out to understand the influence of applied bending loads on ultrasonic guided-waves propagating in laminated composite structures. Composite coupons fabricated from unidirectional plies are instrumented with piezoelectric transducers, and subjected to three point bending test. Load effects on sensing signal parameters such as signal amplitude and phase are studied for wave propagation paths along the fiber direction, as well as paths that are transverse to the fiber direction. Load compensation strategy for addressing load influence on signal amplitude is proposed.
Incorporation of Cracks in Finite Element Modelling of Existing Concrete Structures
Transport infrastructures are fundamental in society and interruption in their service leads to large economic losses. However, inspection and maintenance work of said infrastructures need to be performed to ensure their safety, which traditionally involves limitation or interruption of their use. This paper treats the finite element modelling part of a framework for non-intrusive assessment of existing structures. In practice, the load history of a structure is rarely known in detail. Here, existing cracks due to previous loading is included from the start of the finite element (FE) analysis to circumvent this issue. The focus is on comparing the choice of different shear retention factors for discrete cracks based on comparison to experimental test results. The results indicate that the shear retention factor used for the discrete cracks does not influence the capacity, which remains the same for all considered values. It appears the anchorage force development of the bond-slip reinforcement does not rely on shear transfer in the discrete crack in the adopted FE model. Moreover, the results showed that combining both discrete cracks and a bond-slip relation implicitly including the loss of confinement due to corrosion-induced splitting cracks leads to excessively low capacity compared to experimental results. From the present study it can be concluded that the shear retention factor is of little importance for the considered FE model. However, it is emphasized that other modelling choices may entail other findings.
A Digital Twin of Bridges for Structural Health Monitoring
Bridges are critical infrastructure systems connecting different regions and providing widespread social and economic benefits. It is therefore essential that they are designed, constructed and maintained properly to adapt to changing conditions of use and climate-driven events. With the rapid development in capability of collecting bridge monitoring data, a data challenge emerges due to insufficient capability in managing, processing and interpreting large monitoring datasets to extract useful information which is of practical value to the industry. One emerging area of research which focuses on addressing this challenge is the creation of ‘digital twins’ for bridges. A digital twin serves as a virtual representation of the physical infrastructure (i.e. the physical twin), which can be updated in near real time as new data is collected, provide feedback into the physical twin and perform ‘what-if’ scenarios for assessing asset risks and predicting asset performance. This paper presents and broadly discusses two years of exploratory study towards creating a digital twin of bridges for structural health monitoring purposes. In particular, it has involved an interdisciplinary collaboration between civil engineers at the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and statisticians at the Alan Turing Institute (ATI), using two monitored railway bridges in Staffordshire, UK as a case study. Four areas of research were investigated: (i) real-time data management using BIM, (ii) physics-based approaches, (iii) data-driven approaches, and (iv) data-centric engineering approaches (i.e. synthesis of physics-based and datadriven approaches). A framework for creating a digital twin of bridges, particularly for structural health monitoring purposes, is proposed and briefly discussed.
A Framework for Digital Twin of Civil Infrastructure— Challenges & Opportunities
Digital Twin of an infrastructure is a living digital simulation that brings all the data and models together and updates itself from multiple sources to represent its physical counterpart. The primary focal point of the present study is to propose a framework for Digital Twin of infrastructure and to demonstrate it in the context of a next-generation condition assessment method. The proposed framework is based on the optimized integration of: (1) Structural Inspection: Autonomous Data Collection using drones to minimize intrusion on the transport flow, cover large areas in a minimum of time, access to hard-to-reach areas and minimize exposure to safety hazards for inspectors and users; (2) Damage Quantification: Automated Data Interpretation using data-driven techniques to detect and quantify geometrical and visual anomalies. e.g. cracking and spalling, on the surface and sub-surface of concrete infrastructure; and (3) Performance Prediction: Advanced Structural Simulation combined with physics-based deterioration models to calculate structural performance. The outcome of the study is expected to radically transform the current practices by leveraging drones for inspection, data-driven models for damage quantification, and physics-based models for performance prediction, all seamlessly connected to a living simulation platform “Digital Twinâ€ which updates itself after each inspection round.
A UAV-Based Platform for Real-Time Damage and Defect Identification in Concrete Structures
Measurement Accuracy of Distributed Sensing Fibers Embedded Within Capillaries of Solid Structures
As an initial step of research ‘Embedded fiber optic sensor (FOS) within additive layer manufactured part for structural health monitoring’, this work investigates strain measurement accuracy of the embedded FOS. There are different factors that could influence strain measurement accuracy, and this study singles out fiber position variation within capillaries by using milled Aluminum 6082. To be more specific, three Aluminum 6082 parts are milled, each of which has a straight capillary inside, with diameters 2mm, 4mm and 6mm respectively. Optical fibers are placed into capillaries centers manually and adhesives are filled in between to bond the fiber and specimens. Subsequently, strain measurement accuracy by embedded FOSs is studied both analytically and experimentally. An analytical model is used to calculate theoretical strain of specimens with embedded FOSs. Static four-point bending tests are performed on the three Aluminum 6082 parts with embedded FOSs under 8KN. Based on the analytical model, theoretical strain distribution along the capillary center is plotted and two strain distribution extremes corresponding to fibers on upper and lower edges of capillaries respectively are plotted as well. Comparison of results from both the model and test shows that test results lie within the two strain distribution extremes, and yet discrepancy exists between the test measurement and theoretical strain distribution, which increases when capillaries holding the embedded FOS gets larger.
Durability and Mechanical Properties of Optical Fiber Strain Sensing Cable for Structural Health Monitoring in Harsh Environment
Optical fiber cables are being widely used in structural health monitoring. Thanks to their tiny dimension, the ability to perform distributed strain measurements and to withstand harsh environment, they can be also used in nuclear facilities. The durability of the sensor in such environments has therefore to be assessed. For this reason, mechanical and thermal tests on a custom strain sensing cable have been performed, before and after being irradiated up to 500 kGy. Such custom strain sensing cable includes a special fiber, resistant to radiation, put into a commercial strain sensing cable. Using a Brillouin scattering-based device (with a PPP-BOTDA technique), evaluations of the strain and temperature sensitivities are performed on the different layers of the sensing cable, separately and altogether. This allows evaluating the influence of each component on the cable performances. This includes the fiber in its primary coating, the obtained strain sensing cable and its constitutive component (FIMT, Fiber in Metal Tube). Results point out the importance of a proper calibration of the sensing system: the Brillouin strain and temperature sensitivities depend on the fiber type and on its coatings. The choice of the optimal sensor should be based on the whole sensing chain. Results show also that radiation impact remains moderate over the target sensing range (1 % in elongation). The sensitivity coefficients variation due to radiation also remains very small.
Large Area Electronics Based Sensing Sheet for Strain Monitoring and Damage Detection of Bridges
Damage detection, localization and prognosis are fundamentally important for a comprehensive structural health monitoring (SHM) process. Damage is often associated with an anomalous strain changes or jumps. These changes are best captured if sensors are in direct contact with the damage. However, one-dimensional direct sensing is often expensive to cover the whole structure. Two-dimensional sensors, on the other hand, show promise to allow direct sensing on a large scale. Researchers at Princeton University have developed a flexible thin-film resistive strain sensor based on the principles on large area electronics (LAE). These two-dimensional sheets are suitable for covering large areas ( 10m2) and, hence, could be used for direct damage detection. In this work, the authors explore the suitability of such sheets for real-life applications. Results of laboratory experiments conducted using an aluminum cantilever beam for evaluation of performance in ideal conditions are presented. The strain measurements show an excellent match with analytical results with acceptable standard deviations. Strain measurements on Streicker Bridge at Princeton University campus obtained using these sheets are compared with those obtained from pre-installed fiber-optic sensors. The results show a close agreement between the two, inspiring confidence in the use of sensing sheets for cost-effective strain measurements and direct damage detection for long-term infrastructure structural health monitoring. Finally, the paper is concluded with limitations, sources of error and future possibilities
Full-Length Tunnel Structural Monitoring
New and existing tunnels can present structural risks related to surrounding geotechnical and hydrological conditions as well as unknowns related to design assumptions and construction materials. Such risks can materialize through the apparition of abnormal deformations, cracks, water ingress or, in the worse cases, collapse. The localization of such events or their precursor signs is a-priori unknown, so traditional instrumentation in chosen cross-sections is ineffective for damage detection and localization. Regular visual inspection is more effective in terms of detection probability, but is typically limited in terms of temporal intervals between visits. Additionally, tunnels are often difficult to inspect since the access is restricted due to operational reasons. If such structural risks have been recognized in the design phase or have been identified by inspection, installing a distributed fiber optic sensing system allows a permanent monitoring of the tunnel over its whole length. Sensing cables are typically installed longitudinally along the tunnel length at different positions around the section and provide detection and localization or abnormal deformations and settlements, formation or development of cracks and unusual temperatures. This contribution presents the application of distributed optical fiber sensing to the permanent monitoring of a highway, a railway and a penstock tunnel. For each project we provide information about the system design, installation and monitoring results.
In-situ Assessment of Curvature and Bending Characteristics Along Geotechnical Structures Using Distributed Fiber Optic Sensors
Soil and rock mechanics applications require suitable monitoring concepts to assess the structure’s bearing capacity and the integrity of the structure itself. However, conventional monitoring methods mostly enable either only distributed measurements at the structures’ surface or pointwise measurements inside structures. This paper presents different case studies, in which distributed fiber optic sensors (DFOS) were used to capture strain profiles along grouted anchors, driven ductile piles as well as tunnel cross sections. As a result of the appropriate arrangement of two sensing layers along the monitoring objects, information orthogonal to the neutral axis of the sensing fiber, i.e. curvature and bending characteristics, can also be derived. The DFOS results are compared to conventional measurement techniques to examine the performance of the designed sensing approaches. Due to the distributed sensing feature and the high spatial resolution of about 10 mm, shape changes of embedded objects can be observed without gaps over the entire length, which gives new information about their structural behavior. This paper demonstrates the high potential of DFOS in geotechnical applications and the capability to extend conventional monitoring methods in soil and rock mechanics.
Distributed Sensing to Assess Bond Degradation
It is well known that reinforced concrete beams with low shear span to depth ratios have higher shear capacity due to the change in load carrying mechanism from beam action to arching action. However, up until now it has not been possible to understand the development of these mechanisms, which in turn makes it difficult to assess these beams. Distributed fiber optic strain sensing offers the ability to capture strain distributions along the full length of the reinforcement. When coupled with dynamic sensing capability, it then becomes possible to capture the formation of bond degradation that leads to arching action, as well as the impact that load cycling has on further bond degradation. In this study two beams were tested with identical shear span lengths but different effective depths, resulting in different a / d ratios. One beam carried load through beam action while the other beam carried load, once degradation had taken place, through arching action. The beams were loaded, then cycled for 3600 cycles, before being tested to failure. The behavior of the two beams is investigated by comparing the deflection and longitudinal strain behavior during the load cycles and just before failure. Significant differences in the longitudinal strain distribution developed during the tests, clearly highlighting the formation of arching action in the specimen with the smaller a / d ratio. The results of these experiments could be used to refine assessment techniques such as code models and finite element analysis.
Durability of Distributed Optical Fiber Sensors Used for SHM of Reinforced Concrete Structures
This paper presents an experimental study on the durability of two commercially available optical fiber cables intended for distributed strain measurements and crack monitoring of reinforced concrete structures. The experimental program is based on accelerated aging tests performed on small concrete specimens instrumented with these two embedded optical fiber cables. Pull-out tests are carried out after three months of accelerated aging, in order to evaluate the effect of aging on the mechanical response of the cable/concrete interfaces, as this response is directly related to the strain transfer from the host material (concrete) to the optical fiber core. After three months, the cable/concrete interface of one of the two cables was found severely damaged.
One Hundred Years of Strain Sensing in Civil Structural Health Monitoring
Informed by the theory of structures and materials science, design of civil structures is based on criteria that deal with the stresses and deflections in the structure. Consequently, assessment of stresses and deflections in real structures became one important aim of Structural Health Monitoring (SHM). In general, however, stress and deflection cannot be measured directly in real-life settings due to various limiting factors related to sensing technologies, site conditions, and properties of structural materials. Exceptions exist, and stresses and deflections can be directly monitored in very specific applications, but there are no technological solutions that can be generalized for that purpose. Strain is a parameter which is directly correlated to both stress and deflection. In addition, unusual structural behaviors (e.g., damage or deterioration) frequently have symptoms appearing as strain field anomalies. Hence, strain-based methods were among the first approaches applied in SHM. The first concepts of strain sensing were tackled in XIX century when Lord Kelvin demonstrated that electrical resistance of metallic conductors is related to their mechanical strain, and thus established the physical principle of a resistive strain gauge. However, the first strain sensor to be manufactured and applied in real-life settings is the vibrating wire sensor. Its creation was published in 1919 and the first applications were carried out in 1920’s. Since then, two generations of strain sensors matured and became regular tools applied in strain-based SHM: discrete electrical sensors, and fiber-optic sensors (both discrete and distributed). While the former provided short-gauge sensors that established bases in local strain monitoring, the latter provided long-gauge and 1Ddistributed sensors that greatly extended performance and applicability of strain-based SHM by enabling global structural monitoring and integrity monitoring. Current research aims to create a third generation of strain sensors that target expansion of integrity monitoring from 1D to 2D and 3D approaches, based on various technologies including 2D smart paints and surfaces, and 2D sensing skins and sheets, and 3D selfsensing materials, smart dust and sensing sand. This paper presents 100-year retrospective on strain sensing technologies employed in SHM, with aim to emphasize the milestones in creation of strain-based SHM and identify future directions.
High Accuracy Flight State Identification of a Self-Sensing Wing via Machine Learning Approaches
Motivated by the supreme flight skills of birds, a new concept called “fly-by-feelâ€ (FBF) has been proposed to develop the next generation of intelligent aircrafts. To achieve this goal, Stanford Structures and Composites Lab (SACL) has developed a smart wing which embeds a multifunctional sensor network on the surface layup of the wing [1]. By leveraging structural vibration signals recorded from multiple piezoelectric sensors in the sensor network under a series of wind tunnel tests, data-driven approaches are developed to identify the flight state of this smart wing, i.e. angle of attack (AoA) and airflow velocity. Different preprocessing techniques are used including extracting 38 features in both time and frequency domains and standardizing the raw signals. Various supervised learning algorithms were applied to effectively establish the mapping from the feature space to the practical state space. In addition, it is found that 1D Convolutional Neural Network (CNN) can directly learn features from standardized signals and achieve similar performance to other algorithms using manually designed features. Compared with previous study [2], we have successfully achieved 96.55% test identification accuracy with the airflow velocity resolution improved from originally 3 m/s to 0.5 m/s under the same AoA.
Dynamic Data Driven Applications Systems (DDDAS) for Structural Awareness
Structural health monitoring (SHM) seeks methods to characterize damage detection of systems during operation. One method for SHM is the Dynamic Data Driven Applications Systems (DDDAS) paradigm that integrates high-dimensional first principle models, real-time sensing, and control architectures. Over the last two decades, many DDDAS SHM results support space, air, and network awareness. In this paper, we highlight the opportunities to enhance developments towards systems, structures and materials awareness. In materials awareness, the goal is to use physics and computational modeling to support micro-properties analysis when only macro measurements are available. For structures modeling, the dynamic methods support a finite-element model optimization when only a subset of the sensor mesh has data. As a third example, systems awareness utilizes the environment to model structure properties, where the contextual information is generally available, but not precise enough for nanoanalysis. Future structures awareness can leverage DDDAS methods by capturing models of composites, metamaterials, and nanomaterials combined with advances in artificial intelligence/machine learning for prognosis, sensing, and control.
Rotating Machinery Prognostics via the Fusion of Particle Filter and Deep Learning
Prognostics and health management (PHM) emerges to be a promising technology which enhances the reliability and reduces maintenance cost of rotating machineries. Prognostics, as the key part of Structural Health Monitoring (SHM), focuses on predicting the remaining useful life (RUL) of a system, based on the current and historical monitoring data. RUL prediction algorithms can be roughly divided into two categories: model-based method and data-driven methods. Among the two, data-driven methods are widely used because of the availability of monitoring data and difficulty of developing accurate mathematic physical models. However, traditional data-driven methods ignore the physical mechanism and have limitation in uncertainty modelling of predicted results. To overcome the weakness of data-driven methods and improve the accuracy of prediction, a novel fusion algorithm of particle filter (PF) and long shortterm memory (LSTM) neural network is proposed in this work. By this means, the RUL prediction from LSTM is obtained with confidence intervals through PF. The proposed method is validated using crack size propagation and the RUL prediction using traditional LSTM is presented for comparison. The results demonstrate the effectiveness of the fusion algorithm which integrates the advantages of both PF and LSTM.
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Predictive models for component distress are important for companies working with services and warranties of large fleets of engineering assets (e.g., airplanes, jet engines, wind turbines, etc.). Unfortunately, factors such as duty cycle variation, harsh environments, inadequate maintenance, and manufacturing problems can lead to large discrepancies between designed and observed component lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists can use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A fatigue crack growth test problem is used to present the main features of the proposed recurrent neural network. The results demonstrate that our physics-informed neural network is able to accurately model fatigue crack growth.
Towards Proving Runtime Properties of Data-Driven Systems Using Safety Envelopes
Dynamic data-driven application systems [1, 2] (DDDAS) allow for unprecedented self-healing and self-diagnostic behavior across a broad swathe of domains. The usefulness of these systems is offset against their inherent complexity, and therefore fragility to specification or implementation error. Further, DDDAS techniques are often applied in safety-critical domains, where correctness is paramount. Formal methods facilitate the development of correctness proofs about software systems, which provide stronger behavioral guarantees than non-exhaustive unit tests. While unit testing can validate that a system behaves correctly in some finite number of configurations, formal methods enable us to prove correctness in an infinite subset of the configuration space, which is often needed in cyber-physical systems involving continuous mechanics. Although the efficacy of formal methods is traditionally offset by significantly greater development cost, we propose new development techniques that can mitigate this concern. In this paper, we explore novel techniques for assuring the correctness of data-driven systems based on certified programming and software verification. In particular, we focus on the use of interactive theorem-proving systems to prove foundational properties about data-driven systems, possibly reliant upon physics-based assumptions and models. We introduce the concept of the formal safety envelope, analogous to the concept of an aircraft’s performance envelope, which organizes system properties in a way that makes it clear which properties hold under which assumptions. Beyond maintaining modularity in proof development, this technique furthermore enables the derivation of runtime monitors to detect potentially unsafe system state changes, allowing the user to know precisely which properties have been verified to hold for the current system state. Using this method, we demonstrate the partial verification of an archetypal data-driven system from avionics, where wing sensor data is used to determine whether or not an airplane is likely to be in a stall state.
Data-Driven Stochastic Identification of a UAV Under Varying Flight and Structural Health States
This paper presents the assessment of a stochastic data-driven global modelling framework for data-driven state awareness under varying flight (airspeed) and structural (damage) states. The proposed framework utilizes stochastic Vector-dependent Functionally Pooled (VFP) models that compound data pertaining to different flight states to develop a single “globalâ€ model representing the system dynamics. In this case, the system identification procedure is implemented for an Unmanned Aerial Vehicle (UAV) modeled in ASWING, which is a low-fidelity aeroelastic software for aerody- namic, structural, and control-response analysis of aircraft with flexible wings and fuselages. A series of dynamic simulations is performed for flight states corresponding to varying airspeed and damage size. Simulated signals are used for the stochastic system identification process involving both parametric and non-parametric analyses. The identified VFP model accurately represents the system dynamics and outperforms its non-parametric counterparts. The modelling method is shown to be effective and accurate in identifying the aeroelastic response for an expanded range of flight and structural states using sample data obtained within that range.
Application of Ultrasonic Guided Waves for Surface Roughness Measurement
Surface roughness is inherently a product of manufacturing process and is often undesirable. In surface metrology, the term “roughnessâ€ is typically applied to the highfrequency and short-wavelength parameters of a finished surface. In practical applications, the surface roughness is directly associated with the quality of the manufacturing. This study proposes the use of ultrasonic surface guided waves to characterize the surface roughness amplitude and frequency during the manufacturing process. Finite element modeling (FEM) of acoustic wave propagation along a rough surface has revealed that there is a cut-off threshold for Rayleigh wave propagation, which is indicative of the surface roughness description. This cut-off occurs for a particular ratio of the spatial surface waviness and the acoustic wavelength, and the detection of the resulting wave attenuation and decay characterizes the surface roughness. If such a measurement system can be used in-line with the production process then it could signal adjustment of the material deposition rate when needed to achieve the required product quality.
Evaluation of Independent Component Analysis for Lamb-wave Crack Detection in Metallic Plates
Guided ultrasound waves represent an auspicious approach for usage in future intelligent sensor systems tackling the task of automated detection of defects in critical thinwalled mechanical structures. In these structural health monitoring (SHM) applications, permanently attached active piezoceramic transducers enable repeated non-destructive interrogations, which result in detailed time-dependent multivariate information about the examined structure. In the present work, a novel statistical signal processing scheme based on independent component analysis (ICA) is presented as well as evaluated concerning the task of defect detection. ICA is capable of separating signal components caused by defects from coherent noise. ICA therefore enables subsequent clustering of signals into the damaged and undamaged cases. The evaluation is carried out on numerically generated test data stemming from metallic sheets, which optionally exhibit crack-type defects. For the computer simulation of ultrasonic Lamb-wave propagation, a simple numerical method is introduced which permits the rapid generation of a diverse data set. Results of the evaluation are discussed and an outlook is finally given.
On the Quantification of Adhesive Layer Thickness Effect on PWAS Performance and Damage Detection Capability
monitoring of aircraft and civil infrastructures, a more accurate knowledge of the system is required to increase prognosis reliability. The adhesive bonding layer between a piezoelectric wafer active sensor (PWAS) and the host structure is often pointed out as a critical factor. Several studies have shown behavioral differences with bond thickness and stiffness, from the so-called “shear-lagâ€ effect to the change in PWAS dynamics. Issues such as the role of adhesive layer on the electromechanical impedance of the PWAS; the identification of bonding defects; or the influence of layer thickness at elevated temperatures on Lamb wave response have been extensively studied. However, there are questions that remain unclear, such as its importance for damage detection capability. The main goal of this work is to search for possible correlations among the layer’s thickness, the PWAS admittance and the response to Lamb waves and, additionally, to evaluate the damage detection capability of PWAS bonded with different thicknesses. On the assumption that a high adhesive shear modulus and a thin bond thickness would improve the performance of the sensor/actuator system, the epoxy DP490 (3M) was selected to glue a matrix of PZT transducers to an aluminum plate in columns of 50, 100 and 200μm adhesive thickness layers. Four techniques were used to analyze the system as illustrates the Figure. Firstly, the impedance and admittance of each sensor were measured. Then, a round-robin test, using one PWAS at a time as actuator, was performed in the pristine and damage structure. After that, the complex aluminum-adhesive-PWAS was encapsulated and cut to conclude the actual thickness of the adhesive layer. Finally, a finite element model was implemented for a better understanding of the physical problem. Understanding and relating the effect of bonding on the electromechanical measurements and Lamb wave signals would allow adjusting and controlling the signal processing for damage detection in a wide range of scenarios. Likewise, knowing the frequency dispersion caused by a specific adhesive thickness would help to determine the time of flight, and therefore, the damage localization with higher precision.
Health Monitoring of Composite Structures by Combining Ultrasonic Wave Data
With the growing size of large-scale structures, composite materials are being increasingly used due to their high strength-to-weight ratio. As structures get larger, so too does the necessity for a robust health monitoring system; a well-founded and reliable method of doing so is by monitoring acoustic emissions. However, such a method encounters significant problems when employed on composite structures; such as high dispersion of extensional modes, propagation distance-dependent wave velocity and highly subjective characteristic frequencies of failure modes. Ultrasonic guided waves offer the abilities of location and severity detection to a more accurate level, whereas acoustic emission involves a more efficient passive approach. By analysis of ultrasonic waves in a carbon-fibre-reinforced-polymer (CFRP) plate, information is show here that will provide the stepping stones for the development of a robust health monitoring system for composite structures using ultrasonic guided waves. Advanced signal processing is used to show information on selected features of an acoustic burst as it propagates through the composite material, as well as how damage affects these features. It was found that both the energy of the burst and the first asymmetric mode are affected by the orientation of fibres in a composite, although in differing ways. However, the time of flight of an acoustic burst is unaffected. Subsequently, damage affects all of the above mentioned features, and this knowledge can be utilised to identify damage. Finally, the one-dimensional propagation of a wave along the length of the fibres is tested against various statistical-based models to showcase the potential of this method for use in modelling the propagation of an acoustic wave in a composite material.
Model-based Ultrasonic Guided Wave Inspection to Identify and Attenuate the Effect of Environmental Temperature in Structural Health Monitoring
Ultrasonic guided wave investigation is considered an accurate approach to directly measure the structural variations and achieve the desired monitoring results in a single inspection test. Still there exist some issues in real time morphological investigations, such as sensitivity of propagating waves and piezoelectric sensors to the fluctuation in environmental and operating conditions (EOCs). Structural health monitoring (SHM) setup’s feedback mechanism and reliability of inspection process are affected by the EOCs effect. The ultimate aim of current research is to propose a model- based ultrasonic guided wave inspection scheme to identify and attenuate the effect of environmental temperature on the guided wave signal and PZT sensors. A specific arrangement of sensors and actuators network is designed to achieve optimal results from proposed scheme. A time domain numerical analysis is conducted in ABAQUSTM, and MATLAB® environment is utilized to develop a wave velocity function on the basis of FEM results to measure the group velocity of S0 wave mode. To retain excitation frequencies in certain limit and remove the noise, a low band pass filter is used in current research. Finally, an analytical solution is applied to calibrate the FEM results and validate the proposed scheme. A good agreement is observed between analytical solution and FEM results.
Placement Optimization on Tubular Structures –Towards Application on Tubular Structures
This paper presents an extension of a model, which we have developed for optimization of the number and locations of piezoelectric wafers network on convex and non-convex structures, to tubular structures. The proposed objective function is to maximize the coverage of the network on the structure that is discretized to a set of control points, while minimizing the number of PZT wafers. The tubular structure is modelled as a plate of any given dimensions with the appropriate boundary conditions imposed. In the optimum solution, each control point should be covered by a user-defined number of sensing paths, defined as the coverage level. During the optimization process, any location on the plate is considered as a potential position for a PZT wafer. The algorithm provides the flexibility of changing a wide range of parameters including the number of PZT wafers, the distance covered around the sensing path, the required coverage level and the number of control points. Moreover, since the structure is continuous, the wave can propagate in every direction. Thus, a pair of PZT elements can communicate in two directions and can cover the control points lying in the neighborhood of its two corresponding paths. The developed model was solved using genetic algorithm. Sensor network configurations were simulated on pipe sections. The results were promising and they demonstrated the proficiency of the developed model in distributing the PZT wafers on curved structures.
Baseline-free Method for Acousto-Ultrasonic On-Line-Monitoring of Cracks in Mechanically Loaded and Rotating Axles
In real operation, components are exposed to varying environmental and operational conditions (EOC), e.g. temperature, speed of rotation, etc. For Acousto- Ultrasonics (AU)-based methods in Structural Health Monitoring (SHM), it might be not economically feasible to collect data and construct baselines for all possible combinations of EOCs. In consideration of such practical factors, this paper presents a novel baseline-free method for detection of transverse cracks in axles. The underlying effect is the opening and closing mechanism of the crack during a revolution of a mechanically loaded and rotating axle which leads to a difference in the wave propagation depending on the angle of rotation. During the short time of data acquisition, the EOCs remain nearly constant and the methodology focuses only on the difference in wave propagation due to the rotating stress distribution and the rotating crack. The differences of the structural responses result from the change of wave velocity and reflection behavior. Mechanically loaded and rotating axles can be found in many industrial applications for example in running gears. The presented method actually provides the possibility to realize Condition Based Maintenance (CBM) for safety critical components, which has the potential to significantly reduce the maintenance cost and to increase the availability level of the component. In this study, Piezoelectric Wafer Active Sensors (PWAS) are applied to excite the elastic waves and to measure the structural response during rotation. In situ ultrasonic measurements and an on-line analysis of the structural response were performed during a rotating bending fatigue test of an axle. The experimental results demonstrate the effectiveness of the presented method. The growing crack shows a progressive change of a characteristic pattern.
Investigation of Acoustoelastic Guided Waves Subjected to an Inhomogeneous Prestress
Acoustoelastic guided wave, combining the advantages of acoustoelastic effect and guided waves, provides a promising technique in measuring or monitoring prestress. However, computations of the acoustoelastic guided wave are mostly limited to simple waveguides (e.g. plates and rods) and only take account of normal stress. In this paper, a method is proposed to calculate the acoustoelastic guided waves in waveguides with arbitrary cross sections subjected to an inhomogeneous prestress through combining the semi-analytical finite element (SAFE) method and the acoustoelastic effect. The method is first validated in plates with solutions from superposition of partial bulk wave (SPBW) method and then applied to practically important cases to show capacity of the method. This method can also be extended to calculate acoustoelastic surface waves including Rayleigh wave and Love wave using perfectly matched layer (PML) to simulate the semi-infinite half-space where the response of the surface wave to the depthvarying prestress can be investigated which may provide the potential to reconstruct the depth profile of near-surface residual stress from the dispersion data of ultrasonic surface waves.
Signal Domain Transform-based Damage Imaging for the Complicated Structures
In practical Lamb wave-based damage detection, the spatial resolution of Lamb waves is usually restricted by not only dispersion but also the space duration of excitation waveforms, i.e., the initial spatial resolution (ISR) for the signals before travelling. To overcome this problem, a signal domain transform (SDT) method is presented in this paper. In SDT, the original dispersive Lamb wave signals are transformed from the time to distance domains, with the time-distance scaling on the excitation waveforms particularly modified. Then, both dispersion compensation and ISR enhancement can be actualized to efficiently improve the signal spatial resolution. A high resolution damage imaging based on SDT is further developed. The efficiency of SDT and SDT-based imaging methods has been well demonstrated by the experimental study on an actual complicated aircraft wing beam.
Ultrasonic Guided Wave Dataset for Super-Resolution Reconstruction of Images from Compressed Wavefield Acquisition
Full-wavefield ultrasound imaging is an inspection methodology which provides accurate diagnosis of defects in structures. However, full-wavefield acquisition and processing is often a time consuming process. To tackle this problem, Compressive Sensing (CS) reconstruction algorithms have been proposed to speed up the acquisition process. Such reconstruction can be combined with Super-resolution Convolutional Neural Networks (SRCNN) schemes to obtain high-resolution images from low-resolution wavefield images. This paper proposes the combination of CS and SRCNN to recover images captured with a Scanning Laser Doppler Vibrometer (SLDV), by training the SRCNN with a dataset of ultrasound wave propagation images captured on typical aerospace structures using the SLDV. For the training of the SRCNN, a dataset of 300 structural full-wavefield images were captured on four different aerospace panels and various simulated defects. CS sub-sampling procedures were used to generate a dataset of simulated low-resolution image acquisition. The SRCNN was then trained to recover the original high-resolution images from the low-resolution images obtained after CS sub-sampling. The results demonstrate the capability of the technique for enhancing image resolution and quality while acquiring just 20% of the original number of scan points.
Detection Sensitivity Analysis for a Guided Wave (GW) Structural Health Monitoring System
The paper presents a detection sensitivity analysis for a guided wave (GW) approach to monitoring fatigue crack growth. Piezoelectric beamforming array (PZT) sensors were used to send and receive ultrasonic waves. In this “baselinedâ€ method, changes in sensor response are recorded between the installed and test conditions. Propagating cracks create line-of-site obstacles between arrays, thus more energy is reflected (pulseecho mode) and less is transmitted (pitch-catch mode) between sensor pairs. Any change in acoustic impedance, such as reduction in stiffness or thickness would also cause partial energy reflection, proportional to the relative impedance change. Two test configuration as presented here, including fatigue in 4-point bending and more traditional tension-tension. The purpose of the present study was to evaluate the sensitivity of this GW approach to damage size using the Length at Detection (LaD) statistical approach recently developed specifically to be applied to Structural Health Monitoring (SHM).
Damage Detection in Integrated Circuit Packages Using Ultrasonic Guided Wave and Clustering Algorithm
Reliable detection of interface delamination is important to ensure the reliability of integrated circuit packages with an efficient heat management. In this paper, a robust detection framework is developed to capture sealant delamination between the integrated heat sink and substrate. Ultrasonic guided wave is utilized as an interrogator because of its ability of detecting damages in small and complex structures. In order to efficiently interpret the ultrasonic profile, the noise is reduced through matching pursuit decomposition while minimizing unnecessary loss of information. Then, an automatic feature extraction algorithm is implemented to extract features including amplitude and time-of-arrival. Furthermore, an unsupervised machine learning algorithm, densitybased spatial clustering, is utilized to interpret the extracted features and capture the delamination under different experimental condition. The detection accuracies are compared with existing inspection methods to validate the developed method, and the results from the developed method show good agreements with existing methods while maintaining a relatively high inspection efficiency.
Numerical and Experimental Investigation of Multilayer Delamination Detection in Composites
The extensive use of composite materials in aerospace structures has posed new challenges for implementing guided wave based structural health monitoring (SHM) techniques due to the general anisotropic behavior in composites. Delamination is the most common and dangerous failure mode for composites, because it takes place and grows in the absence of any visible surface damage, making it difficult to detect by visual inspection. Due to the general anisotropic behavior of composites, the successful implementation of delamination detection techniques in aerospace composite structures is always challenging. This paper presents a numerical and experimental investigation to detect the multilayer delaminations in carbon fiber reinforced polymer (CFRP) composite plates using piezoelectric wafer active sensors (PWAS). First, ultrasonic Cscan was conducted to detect and visualize the purpose-built delaminations by inserting Teflon films in a 3 mm quasi-isotropic [-45/90/45/0] CFRP composite plate. Then, a multi-physics three-dimensional (3D) finite element (FE) simulation of the composite plate with multiple delaminations was carried out to understand wave interaction with delaminations, especially the trapped waves due to the multilayer delaminations. Finally, scanning laser Doppler vibrometer (SLDV) measurements were conducted on the composite plate. Wavenumber analysis was used to investigate guided wave interaction with delaminates. It can be found that the wavenumber methods can determine the delaminated region and identify the plies between which the delamination occurs. The results demonstrate a good capability for detecting the presence of hidden multilayer delamination in composite structures.
Parameterization of Spatial Ultrasonic Wavefront via Laser Ultrasonic Technique
Ultrasonic wavefield imaging (UWI) provides insightful spatial information about ultrasonic wave propagation in 2D space. The guided waves propagate with unique patterns in relation to the wave speed and boundary condition for 2D (plate-like) structures. In both isotropic and anisotropic materials, the wavefronts of the incident and reflected waves propagate with unique curves that are distinguishable with UWI. These curve patterns are representable by, and similar to, parameterized polar curves in 2D geometric space. In this paper, several parametric curves (circular, hyperbolic, and cyclic-harmonic curves) were considered. The respective parameters of the parametric curve were set to obtain the closest curve pattern to the wavefront pattern. Then, the 2D laser mirror scanner performed the scanning pattern according to the Cartesian coordinates calculated by the parametric equation as coded into the laser ultrasonic interrogation system (LUIS). An aluminum plate and a cross-ply CFRP plate were used in this paper. The laser scanning patterns of circle, hyperbola, and cyclic-harmonic curve were considered on the aluminum and CFPR plates. Then, the signal responses were plotted as a UWI in angle vs. time domain. The results show that the circular and cyclic-harmonic scanning patterns can tune the incident wavefronts of the aluminum plate and the CFRP plate to arrive at the same arrival times along the scanning path respectively. The hyperbolic scanning pattern demonstrated also the ability to tune the reflected wavefronts to arrive at the same arrival times. With these parameters of the parametric curve, the proposed method may serve an alternative approach for sensing placement implementation in SHM applications. Future work will consider studying the 2D frequency response of the measured ultrasounds in angle vs. time domain to investigate the possible features for damage diagnosis strategies development.
Structural Health Monitoring of Pipelines by Means of Helical Guided Ultrasonic Waves and an Algebraic Reconstruction Technique
The petrochemical industry depends heavily on the networks of pipelines that are used to transfer oil, gas, and biofuels. To ensure normal operational conditions for pipelines, there is a need to develop health monitoring techniques capable to localize various types of defects that are known to cause failure to pipelines. This work presents a novel health monitoring method, based on the use of helical guided ultrasonic waves (HGUW) and a known imaging algorithm, the algebraic reconstruction technique (ART). More specifically, a network of piezoelectric disks are permanently attached on the surface of the pipe and is responsible for exciting and sensing the HGUW. Features from the propagation of these waves are extracted by comparing the undamaged and damaged conditions of the pipe. These features are then processed through ART and the location of the defects are estimated. The main advantage of the proposed methodology includes the ability to drastically increase the inspected area by utilizing the HGUW thus using the minimum number of sensors. In addition, the imaging algorithm is computationally efficient thus allowing for rapid condition estimation. To validate the proposed methodology, experiments, as well as numerical simulations, have been carried out. For these purposes, a 60 cm segment of a long steel pipe has been instrumented with two arrays of six piezoelectric disks each. Damage has been introduced by machining the surface of the pipe in various locations. The same size pipe was modeled using the commercial finite element software ABAQUS where damage was modeled by locally reducing the thickness. A good correlation was observed between the results obtained from the experiments and the numerical model .
Development of a Guided Wave Experiment for the International Space Station
Structural health monitoring (SHM) has gained increasing interest among scientific and engineering communities. Among the many applications of SHM, aeronautics and space-structure development stand out as fields where the environment is especially harsh and managing material deterioration over time is a constant issue. This work reports the progress made in developing experiments designed to explore applications of SHM in space environments with a particular focus on elastic-wave propagation in metallic structures. Active elements of the proposed payload are piezoelectric ceramic sensors/actuators that have demonstrated the ability to withstand the environmental conditions of low Earth orbit (LEO) and are a low-cost option for performing guided-wave testing. The Materials International Space Station Experiment (MISSE) platform onboard the International Space Station (ISS) sets the design specifications of the payload and limits its mass, geometric, and power requirements. The rectangular payload is designed as a layered structure of aluminum plates carrying sensors and electronic hardware. To understand the influence of space environments on the SHM process, ultrasonic guided-wave testing is suggested to infer the variability of the speed of sound in structural materials. A particular payload and sensor layout is proposed to optimize wave transmission for several different guided-wave experiments while still staying within the MISSE based specifications. The anticipated influence of the LEO environment on both the payload and these guided-wave SHM experiments is also considered and modeled. Initial laboratory experiments have demonstrated the detectability of cracks, possibility to infer dispersion characteristics of guided waves, potential to monitor the condition of bolted joints, and strong dependence of the results on the excitation frequency in all of these experiments. The eventual goal of this work is to improve the understanding of the ultrasonic guided-wave response of structural materials in space environments and demonstrate the potential of SHM on the ISS.
Effect of Temperature on Time Reversibility of Lamb Wave in Thin Plates with Surface-Bonded Piezoelectric Wafers
It is important to understand the effect of temperature on time reversibility of Lamb waves as the time reversal process is widely used as a baseline free method for damage detection in thin-walled structures, for which the temperature invariance of time reversibility is an essential requirement. In this work, the effect of temperature on the time reversibility of the Lamb wave in an isotropic plate is studied experimentally for different excitation frequencies. The experiments are performed at three different temperatures. The time reversibility is computed using the similarity index, obtained from the L2 error norm of the reconstructed signal with respect to the original input signal. The study has revealed that there can be a significant change in the time reversibility due to the temperature variations. However, at the best reconstruction frequency corresponding to the maximum similarity index (a concept proposed recently by second authors group), the variation of the similarity index with temperature is insignificant.
Application of the Scattering Analysis Method for Guided Waves Measured by Laser Scanning Vibrometry
This article presents experimental measurement results of scattered ultrasonic guided waves around a damage and continues a preliminary numerical study investigating artificial disturbances as damages. Using a local finite element model so-called wave damage interaction coefficients (WDICs) were simulated, which are compared to measurement results in the current study. For this purpose the out-of-plane wave motion around a damage is measured by a laser scanning vibrometer (LSV). The scattered waves generated by the damage are isolated by using measurements of the pristine and damaged case on the front and back side of the plate respectively. From these measured scattered waves the WDICs can be identified and directly compared to the numerical simulation results. The comparison shows strong similarities between the numerically simulated and experimentally identified scattering patterns.
Multi-scale and Multi-physical-field Coupling Simulation on Detection Mechanism of Aggregates Segregation in CCFST using PZT Based Stress Wave Measurement
The aggregate segregation detection in concrete core of concrete-filled steel tube (CFST) is a challenging task. In this study, numerical investigation on the detectability of concrete aggregate segregation in circular CFSTs using stress wave measurement with piezoelectric lead zirconate titanate (PZT) patches is performed. Multi-scale models of circular CFSTs composed of randomly distributed aggregates without and with aggregate segregation, mortar and interfacial transition zone (ITZ) are established with random aggregates method (RAM). By considering the piezoelectric effect of PZT patches, the coupling effects between the surface-mounted PZT actuator and steel tube as well as that between the embedded PZT sensor and mesoscale concrete core, multiscale and multi-physical-field coupling simulation on the stress wave propagation in the cross section of circular CFSTs with and without aggregate segregation is carried out. A sweep frequency voltage signal is adopted as input for PZT actuator and the effect of aggregate segregation on the embedded PZT sensor measurement is investigated. Numerical results indicate that the embedded PZT sensor response is affected by the aggregates segregation in concrete core. An evaluation index based on wavelet packet analysis on the output voltage response is defined, and its sensitivity to concrete aggregate segregation is analyzed. This study demonstrates the feasibility of stress wave measurement based aggregate segregation detection approach in concrete core of CFSTs with PZT sensors.
SHM of Thin and Small Diameter Tubes Operating at High Temperature for Pitting Corrosion
The use of ultrasonic guided waves has been identified as a promising technology for continuous monitoring of pipe structures to detect small defects of cross-sectional area ratio less than 5%. This paper presents an effective approach to use torsional guided wave mode T(0,1) in thin and small diameter stainless steel tubes operating at elevated temperatures in the range of 150 oC. for detecting small pitting defects using magnetostriction sensors. The phenomenon of scattering of the fundamental torsional mode T(0,1) from complex-shaped discontinuities in thin tubes is closely examined, and its excitation parameters are optimized to an extent using finite element (FE) simulations. From the numerical and experimental results, the optimized design for transmitting/receiving pure torsional modes are investigated. Then, the reflection from three dimensional (3D) notches with varying depth profiles in thin tubes is studied. The results show that the reflection coefficient of torsional guided wave mode T(0,1) is a stronger function of the through-thickness depth and, axial and circumferential extent of the notch. As a result, plots of reflection coefficient as a function of the circumferential extent, axial extent and depth of the defect are shown for particular pipe size. The finite element results are then compared with the experimental data. The sensitivity of the reflected signal from defects up to 2% of the cross-sectional area ratio was examined, and it was found that defects smaller than 2% of the cross-sectional area ratio were detectable. The results from these analyses have been used to propose a practical approach to determine the maximum depth of a pitting defect from the reflection coefficient behaviour, provided that the external circumferential extent or axial extent of the defect is known. This work is of great interest to many process industries including semiconductor manufacturing industries.
Damage Detection and Localization via Cross-Correlation on Metallic Panels Under Ambient Loading
Detecting and localizing damage of aircraft structures using naturally occurring air flow provide a viable means of developing more efficient structural health monitoring systems. Such passive sensing techniques eliminate the need for acquiring power either from the battery or ambients to generate controlled excitation. In a recent study, high-pressure air was randomly sprayed on metallic panels via air blow gun to mimic nonstationary and randomly distributed nature of turbulent flow occurred during operation of the flight. Self-Green’s functions (SGF) were reconstructed using auto-correlation (AC) at 169 (13 × 13) sensing points covering a 150 mm Â´ 150 mm area. Mean square error (MSE) of SGFs between damaged and pristine metallic panels was able to map the damage location in the panel. To inspect a larger area with fewer sensing points, a damage imaging technique based on cross-correlation (CC), reconstruction algorithm for probabilistic inspection of damage (RAPID), and image fusion is proposed in this paper. A flat aluminum panel was tested to validate the proposed idea, and an integrally stiffened aluminum panel was studied. Green’s functions (GF) were extracted between sparsely distributed sensor arrays. Eight sensors were attached to each of the aluminum panels to cover an enclosed area (up to 200 mm Â´ 200 mm) to record the structural response for the pristine and damaged panel. Compressed air was manually swept the panel in arbitrary directions to create nonstationary and random excitation. GFs were reconstructed using cross-correlation, and RAPID and image fusion were used for generating the image of the damage location. The result indicates that the stability of GFs is the key to accurately pinpoint the damage. At least 20 seconds of long-time recording from sensor guarantees such stability, and well-distributed air impact locations results in symmetric GFs. Damage images are in good agreement with the real damage locations. The proposed technique may detect and localize damage utilizing turbulent flow, and it enlarges the inspection region while significantly reduces the sensing points.
Algorithms for Guided Wave Detection of Corrosion in Ground Rods
A key element in the distribution of electric power is the substations, where high voltage power is transformed to lower voltages for distribution to final users. A critical element of the substation is the network of rods driven into the ground that provide the electrical “groundâ€ for the equipment and personnel. As electric substations age, their grounding rod network can corrode and the safety of personnel and equipment can be compromised. Maintaining the integrity of the ground rods is vital to the safe operation of substations, so it is critical to detect if ground rods have corroded to the point where they no longer provide adequate protection to the equipment and operating personnel. In this work, a signal clustering technique was applied to guided wave pulse-echo detection of corrosion damage in grounding rods. The clustering method was tested on 2.9 m long, 19 mm diameter steel rods with machined simulated corrosion pits. Wet clay was applied to the rods to simulate the acoustic loss due to the presence of soil. The corrosion resulting in greater than 50% cross-sectional loss is easily detectable. Defect detection using Fuzzy c-means for clustering produced low false positives and achieved a depth resolution of approximately 0.3 m. The known damage locations on the rods and the algorithm estimated damage locations were in good agreement, even with varying soil loss conditions. Acoustic pulse echo sensors with Fuzzy c-means clustering is a promising tool to survey ground rod corrosion.
Data-Driven Temperature Compensation on Lamb Waves
Active guided-wave-based structural health monitoring techniques have been widely studied for inspecting civil, aerospace and maritime structures. The vast majority of such methods estimate the change in the structures between the times a baseline measurement and the test measurements were made. Temperature changes between baseline and test signal acquisition affects the propagation of the wave in nonlinear and mode-dependent ways. As a result, baseline comparison methods fail when the test and baseline signals are collected at different temperatures. In this paper, we present a novel temperature compensation algorithm that corrects for temperature effects separately for different modes of propagation. Results of experimental data analysis demonstrating that the the method of this paper substantially outperforms the commonly employed baseline signal stretch algorithm are also presented.
A Baseline-Free Fatigue Crack Detection Approach in Orthotropic Steel Deck Using Ultrasonic Guided Waves
A baseline-free methodology utilizing ultrasonic guided waves based on adjacent signal difference coefficients (ASDC) is developed for fatigue cracks detection in orthotropic steel deck in this paper. The crack detection tests are conducted on a test orthotropic steel deck with cracks added under welded stiffeners in advance. Two different cases, i.e. the crack far away from the floorbeam and the crack under the intersection of longitudinal trapezoidal rib and floorbeam have been tested. Locations and lengths of fatigue cracks can be obtained from the adjacent signal difference coefficients. The results show that the proposed baseline-free ultrasonic guided wave method detects and estimates the locations and lengths of the cracks correctly for all cases. This indicates that the proposed baseline-free fatigue crack detection approach has the potential to be applied to the crack detection in actual orthotropic steel decks.
Modelling and Simulation of Ultrasonic Guided Waves Propagation in the Fluid-Composite Structure Interaction
The present work proposes a concept of coupling the ultrasonic guided waves (UGWs) propagation in the fluid-composite structure interaction where the composite elastic structure consists of at least two layers. This coupled multiphysics model tracks the UGWs propagation through the fluid, multilayer structure, and their interface as a part of the structural health monitoring (SHM) system to identify the damage in the structure. The model constitutes a monolithically coupled system of acoustic and elastic wave equations. The solid part comprises the Saint-Venant Kirchhoff (STVK) and neoHookean (INH) material model. The coupling between the two solid layers, and the multilayer structure and fluid inclusion is achieved via the geometric, kinematic, dynamic, interface regularity and boundary conditions. To ensure that the multilayer structure and fluid domains are conforming, we use the arbitrary Lagrangian-Eulerian (ALE) technique. The numerical solution is based on the combination of Finite Element and Finite Difference methods. Discretization is done via a Finite Element method. For this, we follow the Rothe method, in which discretization in time is followed by space discretization, solving the resulting problem according to the monolithic approach. An application example discussed here offers computational support for the off-line/passive structural health monitoring (SHM) systems for composite material and lightweight structure.
Non-collinear Shear Wave Mixing at a Contact Interface: Analytical Modelling and Parametric Investigation
Non-collinear wave mixing provides an attractive approach for detecting various forms of material degradation or structural damage that give rise to a nonlinear ultrasonic response. A new analytical solution for the shear wave mixing at a plane interface characterised by a nonlinear traction law is presented. This analytical solution is derived utilizing a recent approach to describe the scattered field from two contributions that exhibit symmetries corresponding to Mode I or Mode II, in the terminology of fracture mechanics, and a perturbation analysis. The practical value of the analytical formulae for parametric studies is demonstrated and the results are discussed in comparison with experimental results.
Monitoring based Risk Assessment and Asset Management of Civil Infrastructures
ISO 31000 specifies risk being “Risk is the effect of uncertainties on objectivesâ€. The uncertainties in the set of risk indicators can be reduced if we have objective results from monitoring systems. This improves the decisions made in asset management. To achieve a holistic risk assessment progress has been made in standardisation. ISO 21292-2 contains definitions for the indicators related to sustainability of civil engineering structures. The parameters driving the issues of economy, environment and society are addressed complementing the issues of safety, durability and operability. Reduction of uncertainties is a subject in all these categories. EN 16991, published in 2018, is defining a risk-based inspection framework, with a mathematical formulation of degradation (aging) of infrastructures. This allows the prediction of development of condition over time. Applied in asset management the remaining time frame for intervention planning and implementation is calculated. How these new developments have changed the environment for risk-based asset management with monitoring as a key tool to improve the accuracy of predictions is explained.
A Case for Adding an Inspection Level Related to SHM for Bridge Evaluation by CHBDC
The Canadian Highway Bridge Design Code CHBDC), Section 14, uses the concept of a target reliability index (Beta) for evaluating the load carrying capacity of existing bridges. This target reliability index, which is based on the probability of failure and the risk to human life, is related to three aspects of uncertainties inherent in a bridge: those related to (a) element behaviour, (b) system behavior, and (c) inspection level. It is assumed that all bridge inspections are being carried out manually and without using the structural health monitoring (SHM) instrumentations. Citing examples of tests on many instrumented bridges, this paper proposes another level of inspection, which is done with the help of electronic instruments and tests under controlled vehicle loads. The paper proposes simple additions to the clauses of the CHBDC, which can be used to determine the optimum load carrying capacities of existing bridges where structural health monitoring information is available. Using the new value of the target reliability index, the load carrying capacity of bridge can be calculated and being compared to the capacity calculated based on other inspection levels. In this work, using the suggested target reliability index and live load factor, the load carrying capacity of a steel girder bridge is calculated. In this calculation, the available data from structural health monitoring has been used
A Simple Low-Cost Solution for Bridge Scour Monitoring Using Photoelectric Sensors
Majority of bridge failures across North America can be traced back to foundation instability due to hydraulic causes including scour. The inability to effectively inspect scour level at the foundations of bridges present a major risk to our ageing infrastructure. This has led to development of a number of scour monitoring devices such as SONAR, piezoelectric sensors, and tilt sensors among others. However, in addition to limited accuracy and low reliability, the high cost of technologies available today have restricted their widespread acceptance. In this laboratory study, the use of photoelectric sensors to monitor scour around a simulated bridge pier was evaluated. A sediment recess of depth 0.4 m and length 6 m filled with clean sand of D50 size 0.6 mm was centrally located in a flume of total height 1m and 15m length. The simulated bridge pier was mounted at the center of the sediment recess. Five photoelectric sensors were embedded at different depths on the simulated bridge. Two types of photoelectric sensors – throughbeam type, and diffusive type were investigated. To keep the costs low, the sensors were interfaced using the open-source Arduino platform for data acquisition. During the trials, the lowering of the sand level around the bridge pier exposed the sensors thus triggering a change in response. It was found that the sensor output was near zero when buried but jumped instantaneously to 3V (through-beam) and 5V (diffusive) when exposed. To mimic real-life conditions, the scour hole was manually refilled once scour progression ceased and equilibrium scour was reached. It was noticed that the burial of exposed sensors immediately changed the response of both types of sensors back to near zero. Thus, the laboratory tests confirmed that a series of photoelectric sensors embedded at different heights on the bridge pier/foundation can effectively and continuously monitor scour at bridge piers and abutments.
Structural Health Monitoring (SHM) Innovation or Novelty— The Move to Fleet Level SHM
Is the time now? When will SHM transition from a novelty to a decision support platform for Bridge Asset Management. Structural Health Monitoring has been used on major bridges and as a research tool for over 50 years. The instances of its application for long term continuous monitoring are in the 100’s. During this period the condition of the world’s inventory of bridges has steadily declined. Many industry experts are citing the need for better information to manage bridges in a more productive manner. The industry needs to transition to fact based decision making from engineering judgement based on visual inspection. Do those conditions exist now? How will the industry need to adapt to allow for mainstream implementation of measured performance and a timeline for this innovation. This talk will address the technical, organizational and economic requirements for SHM to become a true innovation and be put into successful and practical use as a tool for managing a bridge fleet over their life cycle. The technological state of SHM as of a couple of years ago and the application of internet of things (IoT) technology to structural health monitoring will be discussed. The minimization of required hardware and decentralized data management are some of the major innovations IoT brings to SHM. A case study involving the continuous monitoring of a fleet of 20 bridges is presented. Field data examples using live loads from traffic to estimate load distribution factors are shown. In addition histograms of traffic induced strains on simply supported short and medium span bridge are presented. Examples are presented of timber, prestressed concrete and steel girder bridges. In summary, continuous monitoring a fleet of bridges is now economically and technologically viable. Maybe some words about how data analysis is the future.
Wearable electronics are finding emerging applications in mobile health, rehabilitation, prosthetics/exoskeletons, athletic training, human-machine interaction, etc. However, our skin is soft, curvilinear and dynamic whereas wafer-based electronics are hard, planar, and rigid. As a result, state-of-the-art wearables can only be strapped or clipped on human body. The development of flexible and stretchable electronics offers a remedy for such challenge. E-tattoos represent a class of stretchable circuits, sensors, and actuators that are ultrathin, ultrasoft, skin-conformable and deformable just like a temporary tattoo. Our group has invented a low-cost, dry and freeform “cutsolder- pasteâ€ method for the rapid prototyping of multilayer and reconfigurable wireless e-tattoos. This method has been proved to work for thin film metals, polymers, ceramics, 2D materials and their integration with rigid integrated circuits (ICs). Through a modular and multilayer design, we created e-tattoos that can wirelessly receive power and transmit data through near field communication (NFC) and Bluetooth. Such etattoos can conform to any part of human skin for electrophysiological, mechanical, thermal and optical sensing.
Compressive Sensing Based Indoor Human Positioning Using A Single Thermopile Point Detector
Compressive sensing becomes more and more popular in recent years as it offers a new paradigm for recovering sparse signals from fewer samples than that needed by the Nyquist sampling theorem. This technology shows powerful potential in image systems, radio frequency, communication and signal processing. Recent works about indoor human detection systems based on compressive sensing raise high attention but are suffered from high cost and large deployment effort. In this paper, a low-cost indoor human positioning system that consists of a single thermopile point detector and a rotating mask is proposed based on compressive sensing. A conventional thermopile point detector could only convert the received infrared radiation within the field of view (FOV) into electrical signals. Our approach is to use a measurement mask to compressively sample the radiation within the area of interest. The mask is designed following the pattern of a binary random matrix. Each row of the matrix forms a submask. For each measurement, one sub-mask encodes the radiation within the FOV in a binary manner. With few times of measurements, a compressed signal sequence is obtained. Then, recovery algorithms are implemented to recover the original spatial distribution of the infrared radiation within the FOV from the compressed signal sequence. Due to the existence of the noise of the thermopile sensor, a denoising recovery method is also developed to improve the recovery quality. From the recovered results, the locations of the human can be determined. The results show that the zonelevel positioning can reach high accuracy while the proposed positioning system remains cost competitive and easy to deploy, which has large potential in applications such as indoor monitoring, healthcare and customized heating and cooling system.
An Ultrasonic Stress Wave approach on Quantitative Osseointegration Assessment of a Novel Implant
Quantitative and reliable monitoring of osseointegration will help determine the integrity of the orthopaedic construct, promote novel implant design, facilitate early mobilisation and provide the potential of through-life integrity assessment. The principle of ultrasonic wave propagation has been applied to structural health monitoring in engineering structures for the detection of the onset of damage and its progression. The utilisation of this knowledge is crucial for the through-life integrity assessment of engineering structures. This paper seek to present an ultrasonic wave approach, commonly used in engineering structures, to assess the osseointegration of this novel implant onto a long bone. A series of experiments were conducted using aluminium femur-like and representative implant surrogates. The findings discuss the potential use of stress wave propagation to assess the unification of these surrogates. The study shows an Osseointegration Index using ultrasonic methods can measure the integration of a bone and implant. This also highlights a potential quantitative evaluation technique which is independent of the bone-implant geometry and not affected by the presence of soft tissue.
Physics-Guided Model Transfer for Human Gait Monitoring using Footstep-Induced Floor Vibration
In this paper, we present a physics-guided model transfer approach for robust human gait monitoring across various structures using footstep-induced structural vibrations. Understanding and characterizing the structural vibration caused by human footsteps provides insight about their gait, which is important in smart healthcare applications. Compared to current sensing approaches for gait monitoring (e.g., pressure-based, and mobile-based, and vision-based sensing), vibration sensing is sparse, non-intrusive, and causes less privacy concerns. Conventionally, finding gait-related information using structural vibrations occurs through data-driven inverse modeling with supervised learning, which extracts information about human gaits based on the vibration measurements and corresponding labels. However, the measurements are also affected by the structure properties; therefore, the models developed in one specific structure for mapping the measurements to labels are not applicable to other structures. This results in extensive calibration and training requirements. To overcome these challenges, we present a physicsguided model transfer approach that projects the vibration data into a space where the effect of structural properties is minimized. To this end, we first analytically show that the structural effect in the signal is correlated with the Maximum-Mean-Discrepancy (MMD) data distribution distance across various structures and then, minimize the MMD to find a projected space with reduced structural effect. This reduced structural effect enables us to train a footstep model in one structure (where labeled data is available) and use this model for labeling the samples in the other structures with no labeled data. In field experiments across two types of structures, our approach has achieved 0.95 and 0.97 F1-scores for footstep detection, which is up to a 9X improvement when compared to the baseline approaches.
Vibration Source Separation for Multiple People Gait Monitoring Using Footstep-Induced Floor Vibrations
In this paper we present a source separation approach which enables occupant gait monitoring for multiple concurrent walkers using footstep-induced floor vibration sensing. Occupant gait monitoring is a critical component of human-centric smart home applications like building security, gait health monitoring, and energy management. Current approaches such as vision sensing, wearables, and pressure sensors are limited in real-world deployments due to requirements of line-of-sight, the need for users to wear/carry a device at all times, and dense sensor deployment. Floor vibration sensing overcomes these limitations due to passive, sparse, and non-intrusive sensing, but prior works are limited to “single walkerâ€ scenarios. Real-world gait monitoring, however, typically involves persons walking in groups of two or more people (i.e., “multiple walkerâ€ scenarios). To this end, we introduce a frequency domain-based signal separation approach that enables gait monitoring in “multiple walkerâ€ scenarios by isolating the response from each concurrent walker. The insight is that the overall vibration response is the sum of each individual response signal at an unknown scale (amplitude) and time shift. We validate our approach with real-world walking experiments with two people walking side-by-side and correctly identify the order of the walkers with 88% accuracy (1.8X improvement over a baseline approach).
Video Monitoring of Tourist Density in Cultural Heritage Sites
Cultural heritage sites are of great significance to human and they are the important treasures of mankind. They convey the culture of each nation and country. Therefore, protecting their safety and carrying on cultural inheritance has farreaching significance. At the present time, as the universality of surveillance systems continues to increase, almost all cultural heritage sites are equipped with surveillance cameras. These surveillance cameras allow the administrator to view a display screen at a specific place for remote monitoring. However, currently, in most of cultural heritage sites, the videos captured by these surveillance cameras are monitored artificially and considering the fatigue of people, this method is less efficient. In recent years, with the powerful support of big data and GPU, artificial intelligence technology with deep learning as the core has developed rapidly and has been widely used in many fields. Therefore, this paper proposes to apply the computer vision based on convolutional neural network (CNN) to monitor the number of tourists in an exhibition hall of the Forbidden City by analyzing the surveillance video and then calculate the tourist density in this exhibition hall. After applying the method to every open area in the Forbidden City, by comparing the distribution of the tourist density in each open area, the tourists can better plan their visiting routes and the managers can schedule personnel more easily. The deep learning algorithm used in this paper is You Only Look Once (YOLO) v3. One of the advantages of YOLO v3 is that it has a fast computing speed and the requirements for running platforms is relatively low, which can even be run on mobile terminals. And its accuracy is sufficient for human recognition
Monitoring Pressure Distributions at Human-Socket Prostheses Interfaces Using Graphene-Fabric Sensors
Despite the prevalence of socket prostheses, amputees are at risk of developing pressure ulcers due to the heavy load exerted on the socket during day-to-day use. The complex distribution of soft tissues and bony parts at the residual limb, as well as volume fluctuations that occur over time, make it particularly difficult to predict the time-varying spatial stresses and pressure distribution within the socket. Thus, this research aims to develop a sensing system that is capable of monitoring the pressure distribution in the interior of socket prostheses in real-time, specifically, for characterizing pressure “hot spotsâ€ and for preventing pressure ulcers. The long-term goal is to enable amputees and users to better understand the locations of pressure hotspots and how personalized adjustments of the position and alignment of the prosthesis can improve user comfort and overall performance. In this study, a highly strain-sensitive graphene-based thin film strain sensor was developed and implemented for monitoring pressure distributions at the human-prosthesis interface. Any changes in the applied pressure would alter the local conductivity of the thin film sensor, which can be captured by measuring the voltage distribution along the boundaries of the film. This dataset could then be used for solving an electrical impedance tomography (EIT) inverse problem to reconstruct the conductivity distribution of the graphene-based fabric sensor. Since conductivity is pre-calibrated to applied contact pressures, the result is a live view of pressure distribution. Laboratory tests were conducted to quantify the accuracy of the proposed system. Various cases of controlled pressure conditions were applied at the center of the graphene-based fabric sensor, and EIT results were acquired and characterized. The results demonstrated that this approach could be used in socket prostheses for distributed pressure monitoring.
Stretchable Ultrasonic Transducer Arrays for Three-Dimensional Imaging on Complex Surfaces
Ultrasonic imaging technology has been widely implemented in the fields of medical imaging, non-destructive evaluation, and structural health monitoring. Tradition- ally, rigid and semi-flexible ultrasound probes in current bulky packages can detect the sub-surface defects of a component with a flat surface, but neither is suitable for the accurate defect inspection with three-dimensional curvatures, which significantly constrains the universality and practicability of ultrasonic imaging technology. This study reports the development of flexible and stretchable ultrasound two-dimensional (2D) array probe that exploits an ’island-bridge’ structured interconnection and multi-layer electrodes to integrate miniature ultrasound transducer elements with thin, low modulus silicone elastomeric polymer matrix. The achieved soft device, owning dense transducer distribution, excellent piezoelectric performance, bare internal cross-talk, and over 60% stretchability, is capable of long-time seamlessly conforming on almost any curved com- ponents, and reconstructing the object images with high resolution. With the designed probe, mono-view 2D images of internal defects in a specimen with a convex surface are collected. The entire geometries of two internal defects are reconstructed in threedimensional space. The proposed flexible and stretchable ultrasonic device along with the imaging reconstruction algorithm provides an effective, accurate, and straightforward solution for ultrasonic imaging of complex-shaped structure components.
Remote Sensing Using Augmented Reality and Low-Cost Sensors
The application of sensors in structural health monitoring (SHM) is of utmost importance, as they record the behavior of structures and help structural engineers to make and prioritize informed decisions about necessary repairs, replacement of infrastructure or maintenance procedures. However, although many of the sensors that are currently used in SHM provide the necessary quantitative information, it is difficult to collect it in the field, and they require further processing of data obtained in the field later in the office or at the decision-making headquarters. Such an approach to SHM delays decision making. Wireless sensors networks (WSN) are designed to analyze the data so that the decisions could be performed remotely. However, there is still a gap between remotely operated WSN and the way decisions are performed in the industry. In general, the industry relies on inspectors’ decisions. If WSN could provide direct information to inspectors, the industry decisions would be improved by the obtained data. This paper shows that the availability of WSN data to inspectors can be improved through the application of Augmented Reality (AR) tools. AR is a tool which overlays the known attributes of an object in the corresponding position on the computer screen. In this way it allows integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. For the SHM purposes, AR permits engineers to directly interact with the real world and it enables visualization and integration of SHM with inspectors at the field. For example, HoloLens make it possible to visualize information coming from the sensors in a graphic form in real time. Specifically, the results of the research carried out by the authors of this abstract indicate that the application of HoloLens enables effective communication from low-cost sensors (Accelerometer, Strain gauge, and Laser TFMini - Micro Lidar module) to a database in real time in a reliable way.
Unmanned Vehicles for Lightning Protection System Inspection and Maintenance
To date unmanned vehicles have been used extensively for performing structural inspections. These inspections have generally involved visual sensing modalities such as using conventional visual imagers or infrared imagers to look for thermal signatures indicative of structural damage. The next generation of unmanned vehicles will go beyond these non-contact inspection tasks and will begin looking at performing physical interactions with the structure in order to assess the physical integrity of the structure and to perform actual repair work on the structure itself. In this work we specifically explore the use of unmanned vehicles to perform inspections and repairs on lightning protection systems for infrastructure such as buildings and chimneys. Unmanned vehicles are a particularly attractive solution for this problem because current practice involves exposing inspection/repair technicians to the dangers associated with working at height. To ensure the safety of the inspectors it is generally necessary to install fall-protection systems or erect railings to prevent technicians from falling off roofs and chimneys. Here we begin exploring the use of unmanned vehicles to inspect the structural integrity of lightning protection systems, clean their associated ground wires, and check the tightness of installed lightning rods. The ultimate goal of this work is to develop an unmanned vehicle solution for enabling low-cost inspection and repair of lightning protection systems.
Subsurface Infrastructure Asset Monitoring with Geophysical Sensors and Augmented Reality
Monitoring and assessing the condition and location of subsurface infrastructure is challenging, primarily because the assets are buried underground, often in uncertain locations and often in congested 3-D configurations. Geophysical instruments, such as ground penetrating radar, acoustic pipe locating and leak detection, and magnetic detection are nondestructive mobile sensors that detect, locate and help to assess subsurface features from electromagnetic and acoustic wave interactions. Difficulties with collecting, processing and interpreting the data are due to inaccurate localization techniques for registering the position of the sensors, the mixed formats of the data streams and legacy mapping information, and the large volumes of raw data. This paper will describe how augmented reality tools can alleviate some of these issues with the following techniques: 1. Use photogrammetric techniques combined with above-ground urban 3-D surface maps to localize the position of mobile sensors, even in urban canyons with poor GPS reception; 2. Presentation of 3-D data to users on sight in an augmented reality format, both to aid in data collection and post-collection data presentation; Presented will be results from field tests deploying various aspects of the technology, including ground penetrating radar and acoustic pipe locating of urban subsurface utilities combined with Unity-based augmented reality instruments.
Automated Infrastructure Inspection Based on Digital Twins and Machine Learning
One of the key challenges in our modern society is the provision of safe transport infrastructure. Infrastructure managers are subject to regulations requiring major infrastructures to be periodically checked for damage before it becomes a safety hazard. In the standard structural inspection, specially qualified civil engineers travel to the object to be inspected on site. Notes, sketches and photos are prepared for the subsequent report. The inspectors are introduced to non-directly accessible locations with special, heavy inspection equipment. During such a test the object is not or only partially usable, which leads to interruptions, delays, traffic jam and thus considerable non-availability costs. In recent years, approaches of a drone-based structural inspection are increasingly noticeable. These are mainly limited to a visual inspection of the created optical images. By using new technologies, a more objective and faster structural inspection can be carried out at a lower cost. In order to achieve the highest possible level of automation, the actual test is no longer performed on the real object, but on a digital twin of the construction. The assessment of damages and reporting is carried out automatically.
SHM of Bridges by Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Clustering
Structural health monitoring (SHM) is a very broad field and it is fast growing. For instance, it is being used to identify damages in civil structures by using increasingly modern systems and tools, both to obtain data and for its subsequent processing and analysis. The advancement of technology in data processing and data mining have demonstrated the efficiency of supervised and unsupervised machine learning algorithms in different fields such as medicine, biology, finance, aeronautics and engineering. However, still a problem is the limited application to real civil structures, especially in bridges, which deserves a more exhaustive study in this field. When dealing with vibration data, in many cases, the Fast Fourier Transform (FFT) is used to obtain the damage sensitive features. However, for non-stationary and non-linear signals the Hilbert Huang Transform (HHT) is more efficient considering Empirical Mode Decomposition (EMD) method to decompose the signal into its main components. The present paper shows the feature extractions using an Improved Completed Ensemble Empirical Mode Decomposition with Adaptive Noise technique (ICEEMDAN) and the damage identification and localization by a clustering-based approach. The effectiveness of the methodology is shown using a real case of study in which four structural damage scenarios were imposed in a Warren truss bridge and the vibration caused by a crossing vehicle was recorded by accelerometers. The clustering results showed good correspondence with the damage scenarios located in different bridge zones and, therefore, the proposed approach demonstrates the feasibility for damage feature extraction as well as damage identification and localization.
A Kriging Surrogate Model for Structural Health Monitoring of Miter Gates in Navigation Locks
Structural health monitoring (SHM) plays a significant role in monitoring the performance of the inland waterways civil infrastructure such as navigation locks. The United States has s $500B replacement value of its inland waterway infrastructure. Unexpected shutdowns of miter gates in navigation locks caused by any kind of damage produces tremendous economic loss, particularly if the shutdown is unplanned. Sensing is the first step in any process of damage detection strategy that collects data from sensing network. Lock gates are normally instrumented with strain gauges. A missing contact (or gap) between the concrete supporting wall and gate, one of the most common failure modes of lock gates, creates undesirable overloads and produces different levels of damage. An inverse model is needed to map the data extracted from the strain gauges to damage-sensitive features that are used to build degradation models and make decisions. An inverse Bayesian analysis is performed to identify damage by using strain gauge data. Although the posterior distribution estimations could provide probabilistic solutions to the damage identification, these models usually have high computational cost, and thus they are not suitable of performing real-time health monitoring. In this preliminary work, a Kriging or Gaussian process regression (GPR) inverse model is designed as a surrogate model of the finite element model of the Greenup lock miter gate, to map the strain gauge data to the damage size (i.e. gap length in miter gate contact). Hydrostatic loading scenarios governed by upstream and downstream heights with varying gap length are considered in the design of the Kriging model. Both training and testing data sets are predicted using the designed model. Overfitting problem caused by high nonlinear behavior of the data are solved. The results illustrated that Kriging, as a surrogate model, is a fairly reliable replacement to the computationally expensive inverse finite element model in damage identification. In addition, prediction performances using other Kriging models with different basic covariance functions and sizes of the training data set are compared.
A General Representation for Assessing the Similarity of Structures
One of the key challenges in SHM is obtaining information about the damage condition of a structure in order to detect and diagnose damage when it appears in a healthy structure. For large civil structures, such as bridges, it is simply uneconomical to damage the structure once it has been built in order to obtain the damage case. To this end, a method for assessing the similarity of structures using a new technique called irreducible element modelling is presented. By accurately assessing the similarity between structures, it may be possible to infer the damage state of one structure from the other; the first step is creating a system for assessing the degree of similarity between structures. The irreducible element (IE) model seeks to create a graphical representation of a structure by breaking it down into its constituent parts, described as irreducible elements. In the case of a bridge, these elements could be the deck and the supports. IEs should be well-defined dynamically, e.g. beams, plates and shells. These IEs will form the vertices in the graphical representation, and the edges will represent the joints between each element as well as the boundary conditions for the system. The graphical representation will be attributed with information regarding the geometry and dimensions of these constituent parts, alongside information regarding their material properties. From this representation, the dynamic properties of the structure can be inferred. The IE model also contains information about the boundary conditions and joints within the structure. This paper describes how to construct an IE model for ‘simple’ systems, showing the amount of information that is required to define the model, and how this can be used to create a graphical representation of the structure for comparison purposes.
Grey-box Modelling for Structural Health Monitoring: Physical Constraints on Machine Learning Algorithms
The use of machine learning algorithms for structural health monitoring remains a powerful and active area of research as the technology and availability of data evolve together. The amount of interest reflects the fact that many tasks in SHM processes rely on regression, clustering or classification, which are naturally handled by machine learners. Within the authors’ research group alone, among many examples, regression via neural networks and Gaussian processes have been used for dealing with confounding influences in monitoring data, and clustering methods like Gaussian mixture models and more advanced variants have been used to group data according to operating/environmental and damage condition. In the work that is particularly relevant to the example used in this paper, Gaussian process regression has been used to attempt to model the loads experienced by a structure in operation from (for example) acceleration measurements taken at accessible locations away from where the load is applied. These regression models work well to reproduce measured strains on data unseen by the algorithm, however, they are limited by their dependence on training data (such a model requires training data across all possible operating conditions) and a lack of physical interpretability. Currently the authors are working on means of enhancing machine learning algorithms with physical insight. If one considers an entirely physics-based model as a white-box model, and a purely data-driven machine learning model as a black-box, then the work here is about the development of grey-box models. In this paper the idea of building physical constraints into a Gaussian process regression model is considered. By building knowledge of the physics at work into the kernel, one can ensure that the model predictions are, in the least, physically possible. This paper demonstrates how one can build knowledge of boundary conditions of a vibrating structure into the covariance matrix and how this can improve a GP’s predictive capability.
Machine Learning based Tensile Force Estimation for PSC Girder using Embedded EM Sensor
The actual tensile force of pre-stressed (PS) tendons of a pre-stressed concrete (PSC) girder is one of the important factors for evaluating the performance of PSC girder bridges. To measure the tensile force of the PS tendon, this study proposed a machine learning based tensile force estimation method using embedded elasto-magnetic (EM) sensors. The magnetic hysteresis of PS tendons are changed according to the applied tensile force. To measure the magnetic hysteresis of PS tendon of PSC girder, the EM sensor should be embedded in the PSC girder because the PS tendons were located in inside of PSC girder. The radial basis function network (RBFN), one of the machine learning method, was used to estimate the tensile force using the variations in magnetic hysteresis. To verify the proposed method, the in-field tests were performed. The embedded EM sensors were embedded into PSC girder specimen and the magnetic hysteresis changes due to the variations in tensile forces were measured using embedded EM sensors. The tensile forces were estimated using trained RBFN and they compared with reference tensile forces measured by hydraulic jacking machine. According to the measurement results, the proposed method can be a one of the solution to monitor the tensile force of PS tendons.
On Current Trends in Forward Model-driven SHM
Forward model-driven approaches to Structural Health Monitoring (SHM) are a category of methods in which validated physics-based models are used to generate data for machine learning classifiers. These approaches were developed to address the lack of available damage state data; which is often a problem in many SHM contexts due to it being impractical or infeasible to collect. Many data-driven approaches to SHM are successful when the appropriate damage state data are available, however the problem of obtaining data for various damage states of interest restricts their use in industry. With this aim, several forward model-driven techniques have been developed in recent years focusing on issues in generating validated physical-models that produce damage state predictions without the need for a damage state data. This paper presents the current state of forward model-driven techniques within the literature. In addition, several key technology areas are highlighted with a demonstration of the benefits and challenges a forward model-driven framework provides.
Damage Identification of Sandwich Panel with Truss Core by Integrating Deep Learning Technique
This paper reports some preliminary work in IMECH, CAS on integrating deep learning technique to identify internal damages in the sandwich panel with truss core. Cell-missing and unbound nodes are two main types of damages to be concerned. Numerical models of panels with various damage features are presented to provide original structural dynamic responses, and a vibration-based damage index is proposed to extract damage features, therefore image datasets of randomly damaged samples are constructed for training and testing. The training and testing processes are conducted by Faster R-CNN (regions with convolutional neural network). Results reveal that the proposed method can identify damage location and extent in reasonable precision.
Hierarchical Convolutional Neural Networks Information Fusion for Activity Source Detection in Smart Buildings
Detecting the source of activities in buildings is of interest to enable the potentials for Smart Buildings in industrial facilities, public infrastructure, hospitals, and commercial or residential buildings. This problem has been addressed using intrusive techniques, including vision-based methods. Recent studies have instead demonstrated that unobtrusive approaches could provide comparable results to the intrusive methods. These methods have the capability to identify and classify atypical floor vibration signals to specific sources, e.g., human fall detection or rotary machines fault detection. Most successful applications of vibration-based monitoring use traditional feature learning methods. These methods extract the temporal and spectral features from the vibration signals, and human experts are involved in selecting the features for classification. However, they typically require a labor-intensive process, which may add uncertainty and bias to the results. Several promising alternatives to these approaches have recently been proposed which take advantage of the time-dependent frequency spectrum of the vibration signals (i.e., spectogram). These representations are then fed into a deep convolutional neural network (CNN) architecture for image-like classification, training an accurate model. However, activty source detection in buildings requires the identification of numerous possible sources (classes), which may lead to low accuracy and confidence in the results. Thus, in this study, we incorporate prior knowledge of a target application, called a hierarchical structure of activity sources (classes). We develop a hierarchical CNN-based technique, which has a relatively easy setup, is modifiable, and has an extendable structure, to use floor vibration signatures to detect the source of activities in a building. We validate the method on a recently released benchmark dataset for human activity identification by implementing a flat numerous-class CNN-based approach, and the proposed hierarchical CNN-based approach. The results indicate that the hierarchical CNN-based approach outperforms other methods.
Self-Sensing 3D Printed Repair for Concrete Substrates
Continuous monitoring allows informed maintenance to more effectively prolong the lifespan of civil infrastructure. In this paper, we outline a 3D printing process for smart materials that could simultaneously support the distributed sensing and maintenance of reinforced concrete structures. The smart material employed in this research is alkali-activated metakaolin. It is already well-known that alkali activated metakaolin can attain comparable mechanical characteristics to ordinary Portland cement and is thus able to repair and restore the structural integrity of damaged concrete infrastructure. However, these materials also exhibit a higher electrical conductivity than conventional cements due to the presence of free ions in their matrix. This allows temperature and strain to be monitored through variations in electrical impedance. Here, we demonstrate additive manufacturing of these materials to allow remote installation, with greater design flexibility and repeatability. In this paper, the electrical response of printed samples is outlined, and we discuss how sensor data can be interpreted to detect strain variations for structural health monitoring applications.
Topological Design and Characterization of Piezoresistive Nanocomposites
Piezoresistive nanocomposites have been extensively studied because of their broad applicability for various sensing applications (e.g., monitoring structural integrity and human performance). The piezoresistivity of many polymer nanocomposites stems from conductive/semi-conductive nanofillers’ intrinsic piezoresistivity, tunneling effect, and contact resistance changes of the nanofiller networks as they are strained. Although various nanocomposite strain sensors have been developed using different material systems and manufacturing techniques, their empirically guided fabrication approach can be laborious, inefficient, and, most importantly, unpredictable. Therefore, this study proposes a topological design-based methodology to strategically manipulate the strain sensing performance of these nanocomposites to achieve a wide range of optimized strain sensitivities using the same material system. To demonstrate this, carbon nanotube-latex nanocomposite thin films were spray-fabricated onto patterned polyethylene terephthalate substrates pre-designed with stress-concentrating and stressreleasing topologies. The strain sensing performance of nanocomposites of different topologies was characterized and compared. Furthermore, the mechanical and electromechanical properties of the thin films were also characterized using numerical models. Both the experimental and computational results indicated that the stressconcentrating topologies could enhance strain sensitivity, whereas the stress-releasing topologies significantly suppressed bulk film piezoresistivity.
Damage Detection Via Electrical Impedance Tomography in a Filament Wound Glass Fiber/Epoxy Composite Tube with Carbon Black Filler
Fiber reinforced polymer composites are increasingly being used in the automotive, aerospace, and many other sectors owing to their superior specific stiffness and strength. Filament winding provides means to manufacture cylindrical fibrous composites finding applications in building stronger pressure vessels and corrosion-resistant piping systems. With the expanding use of composites to build vital structural components, there arises the need to monitor the health of these structures. The primary challenge in health monitoring of composites involves the detection of nonvisible and sub-surface damage. Current methods to address this issue in filament wound composites include embedding optical fiber or piezoelectric sensors during the manufacturing process. Apart from introducing weak spots in the structure, embedding these sensors make the manufacturing process difficult and time consuming. Nanofiller modified matrix composites impart conductive properties to the composite which can be leveraged for health monitoring with minimal changes to the manufacturing process. The self-sensing capability of these composites can be combined with conductivity imaging modalities such as electrical impedance tomography (EIT) for damage identification and localization. EIT is a low-cost, real-time imaging technique in which the electrical conductivity of the body is inferred from boundary voltage measurements. To date, however, EIT has been primarily used on planar geometries such as rectangular composite coupons. Therefore, this work demonstrates the potential of EIT for damage detection in non-planar, multiply connected domains – a carbon black (CB)-modified glass fiber/epoxy filament wound composite tube. The results show that multiple through holes as small as 7.94 mm can be detected for tubes with a diameter-to-length ratio of 1:2. It was also observed that the sensitivity of this method improved as the aspect ratio of the tube decreased. These preliminary findings strongly indicate the potential of using EIT for damage detection in more complex geometries such as self-sensing filament wound composite tubes.
Sensing Capabilities of Carbon Nanotube Reinforced Adhesive Films in Joints with Dissimilar Materials: An Approach from Coupon to Sub-element Level
This work aims to investigate the crack propagation monitoring capacity of carbon nanotube (CNT) reinforced adhesive films in joints with dissimilar materials. To achieve this purpose, Mode-II standard tests have been carried out while the electrical response has been determined in terms on electrical resistance measurements. It has been observed that electrical and mechanical response show a good agreement, with an increase of the electrical resistance with crack length due to the breakage of electrical pathways. Brushed and grit-blasted metallic substrates were tested and some significant differences between electrical response have been observed. Samples with poorer surface treatment showed a more unstable response due to a continuous creation and breakage of electrical pathways while grit-blasted specimens present a uniform electrical resistance increase. These results were also stated by microstructural analysis, showing the presence of weak areas in case of brushed substrates. In addition to that, skin-stringer elements were also tested at static conditions, showing a good electrical continuity. Therefore, the potential and applicability of the proposed technique for SHM of bonded joints have been demonstrated
Understanding Constitutive Relationship Between Mechanoluminescence of ZnS:Cu-PDMS and Strain
The research objective of this study is to deepen understanding about mechanoluminescent (ML) characteristics of the copper-doped zinc sulfide (ZnS:Cu)-based elastomeric composite, which is one of two functional building blocks of the mechano-luminescence-optoelectronic (MLO) composite, for advancing design of the MLO composite-based self-powered strain sensor. First, atomic structures of ZnS:Cu, which plays a pivotal role as a mechanical-radiant energy convertor is analyzed using Raman, Fourier-transform Infrared (FTIR), and photoluminescence (PL) spectroscopies under mechanical vibrations that trigger ML phenomena. Second, photometric analysis is performed to characterize ML light emission when the composite is subjected to tensile loading and unloading cycles. Last, constitutive relation between ML light intensity and strain is studied using two high-speed cameras and digital image correlation (DIC) technique.
Additively Manufactured Sensors for SHM of Composite Structures
Polyvinylidene fluoride (PVDF) is a widely used material for properties such as piezoelectric sensing, good chemical resistance, high thermal stability, low acoustic impedances and flexibility. Lead zirconate titanate (PZT) is a piezoceramic with its perovskite phase having good sensitivity and high operating temperatures which has number of applications as ultrasonic transducers and piezoelectric resonators. There is much need of synergizing the sensing and actuation capabilities of both the materials and thus expanding the potential applications on lightweight composite structures with complex geometries. Fused layer deposition is one of the additive manufacturing techniques which helps in achieving the above objective. In this paper, we study the 3D printing of solvent assisted PVDF-PZT nanocomposite sensor and their integration into smart composite structures. 3D printable piezoelectric material is synthesized by the optimum dispersion of PZT nanoparticles to PVDF that is dissolved in solvent. Piezoelectric sensors are 3D printed on the fabric composite surface to study the strain effects during deformation. Electrode printing is carried out using screen printing technique. Impact tests are carried out to study the transient response of the sensors. Dynamic response of sensor is also characterized on an electrodynamic shaker. The effect of strain on the electrical impedance characteristics is studied. Overall performance indicates that these 3D printed sensors can have potential applications in SHM of composite structures for aerospace applications, IoT and robotic automation
Tunable Hybrid Acoustic Metamaterials for Structural Health Monitoring
Metamaterials technology offers new possibilities for wave imaging and for the control of propagation of wave at subwavelength scales. Acoustic metamaterials can manipulate elastic waves in ways that are not possible in conventional materials. Despite much progress in the development of metamaterial subwavelength designs in both optics and acoustics, the extension of this concept to the beam-forming and beam-steering in Structural Health Monitoring has not been attracted much attention. For Structural Health Monitoring perspective, using acoustic metamaterials are more suitable due to the flexible focused and steerable excitation and not need complexity transducers required by phase array technology. Here, we report the demonstration of a tunable hybrid acoustic metamaterial. The proposed concept is based on the design of dynamically tailored structural elements via embedded combination resonant and non-resonant structure. Such a tunable hybrid acoustic metamaterial can provide novel and important functionalities for manipulating the lamb wave and expand their application in Structural Health Monitor- ing.
Feasibility of a New Nonlinear Ultrasonic Technique for Monitoring Damage in Composite Plates
In this paper, the feasibility of a relatively new nonlinear ultrasonic technique called sideband peak count (SPC) is investigated for monitoring progressive damage in composite plate specimens. Lead Zirconate Titanate or PZT transducers are used to generate guided ultrasonic waves in composite specimens. Multiple sets of composite plate specimens are fabricated, damaged and inspected using PZT transducers. While fabricating the composite specimens thermoplastic resins are used because of its t i r a l u p o p g n i s a e r c n i y a v n i r i f s u o i e s d l . Whi e l e t a l p e h t n i e g a m a d g n i c u d o r t nio specimens by impact special attention is given t the understanding of the failure mechanisms of composite materials under low velocity impacts due to the occurrence of invisible damages. The difficulty in the prediction of the initiation and propagation of failure modes in composite laminates comes from the fact that the specimens are not transparent and hence inner damage evaluation is not possible from simple visual inspections. Being able to ‘see’ the internal damages is a basic requirement for understanding the failure mechanisms. Different failure mechanisms, such as fiber breaks, matrix cracking, debonding and delamination are observed for composite materials. The setup for the experimental impact tests is capable of both traditional in air and non-traditional in water impacts. For a comparative investigation, the composite samples are divided into two groups - glass fiber composites and basalt fiber composites. They are damaged by impactors striking the specimens with different levels of impact energy. A chirp signal is excited and propagated through undamaged and damaged specimens in a single sided excitation/detection setup to investigate the effects of varying degrees of damage on the recorded signals. Processing of the recorded signals for damage analysis involved both linear and nonlinear analyses. Linear ultrasonic parameters, such as the change in the time-of-flight of the propagating waves, Fast Fourier Transform and S-Transform of the recorded signals are studied while the Sideband Peak Count or the SPC analysis is carried out as part of the nonlinear ultrasonic analysis.
Third Harmonic Shear Horizontal Waves for Material Microstructural Degradation Monitoring
Detection of incipient damage in structures through material degradation monitoring is of paramount importance for the residual life prediction and maintenance decision. Nonlinear guided waves, through their interaction with micro defects in materials, show promise for pinpointing structural damage at the early stage of its initiation. This study explores the use of the third harmonic Shear Horizontal (SH) waves and experimentally investigates their abilities to monitor microstructural changes in a metallic plate. To this end, a dedicated comparison test was designed, which allowed the generation and the reception of both Lamb waves and SH waves in the same plate, using PZT and Magnetostrictive Transducers (MsTs), respectively. The configurations of the PZTs and MsTs were carefully designed so that the material nonlinearity of the plate (MNP) dominates the nonlinear responses. Thermal aging treatment was applied to the plate to tactically change its microstructure within a controlled heating area. Changes in the microstructural properties of the plate were simultaneously monitored through a Vickers hardness test. Results show that the third harmonic SH waves exhibit a much higher sensitivity to the heating-induced microstructural changes than the commonly-used second harmonic Lamb waves. The material degradations characterized with the third harmonic SH waves were further confirmed by the Vickers hardness test.
Quantitative Characterization of Hypervelocity Debris Cloud- induced Pitting Damage in AL-Whipple Shields Using Nonlinear Ultrasonic Waves
In a typical Whipple shield for protecting spacecraft from hypervelocity impact (HVI), the debris cloud, formed by the shattered material of the outer bumper layer, commits multitudinous, disorderedly scattered pitting craters and cracks over a wide region in the rear wall layer. Material degradation due to the pitting damage is a precursor of structural fragmentation and system failure. In this study, microscopic material degradation of the rear wall layer in a typical dual-layered Whipple shield, initiatedandintensifiedbythedebriscloud-engenderedpittingdamage,ischaracterized using metallographic analysis. Diverse microstructure changes (e.g., refined grains, dislocation,micro-voids andmicro-cracks) areobserved, accompanyingthe generation of visible macro-scale pitting craters, and intensification of material plasticity and nonlinearity. In addition to the material nonlinearity in the vicinity of pitting craters, micro-voids and micro-cracks are also developedwhich distort propagation of probing guided ultrasonic waves GUWs, thereby triggering acoustic nonlinearity. Targeting at the evaluation and monitoring of this sort of pitting damage, an insight into the generation of high-order modes in GUWs is achieved, and then validated via experiment. On this basis, a monitoring and evaluation framework based on the lead zirconate titanate (PZT)network, in conjunction with theuseofthe developednonlinear damage indices, is developed, whereby the hypervelocity debris cloud-induced pitting damage can be depicted and characterized quantitativelyand precisely.
Dynamics-based Testing to Localize Macro Cracking Due to Alkali-Ailica Reaction in Concrete
This work focuses on the diagnosis of degradation in concrete structures caused by the alkali-silica reaction (ASR): a chemical reaction between the cement and certain aggregates containing amorphous silica. The suitability of vibro-acoustic modulation (VAM), is investigated for the detection and localization of cracks caused by ASR. In a VAM test, the structural component is excited using two frequencies. The frequency modulation appears as sidebands around the higher (probing) frequency in the power spectral density (PSD) of the measured response in the neighborhood of the damage zone. A map of the magnitude of such sidebands can be used to detect and localize the damage. We conduct laboratory experiments on four concrete blocks with varying levels of reactive aggregates and different amounts of steel reinforcement, and show that VAM-based testing with optimized test parameters and suitable sensor density can potentially be used to detect and localize cracks in thick concrete structures.
Implementation of Ultrasonic Coda Wave Interferometry on a Real Bridge
Urbanization becomes increasingly important in our modern life. This is undoubtedly a huge opportunity and challenge for construction industry. More and more bridges are constructed to improve the infrastructure and develop the economy. For more than a century, reinforced concrete bridges are the most common bridges due to its durability, relatively low maintenance cost and rapid construction. In general, concrete bridges are designed to ensure a service life of at least 100 years. However, mechanical and environmental factors or excessive use accelerate the deterioration of the bridges. Some bridges even failed in their early-life. Thus, we are faced with the necessity to monitor health condition of bridges at every moment during their life which is also the aim of Structure Health Monitoring (SHM). Most deterioration and failure mechanisms are connected to the development of micro- or macro-cracks. Ultrasonic Coda Wave Interferometry (CWI) method using diffuse wave is one of the most promising method to detect subtle changes and cracking in heterogeneous materials such as concrete. Moreover, the data acquisition system has a high portability and the size of data recorded is small. If needed, even real-time assessment can be performed with an optimized algorithm. Embedded ultrasonic sensors can be installed inside the structure to avoid changes in coupling or excess influence of environmental influence factors. The measurements will focus more on the internal of the structure. Research shows that CWI has high sensitivity in detecting temperature change, stress variation and cracks in laboratory size concrete specimens. A young reinforced concrete road bridge in Gliwice, Poland has been monitored in a first step. Results have shown that the bridge is in good condition. However, the method has so far not been implemented on existing middle-aged bridge. In this paper, the monitoring of a 46 years old reinforced concrete road bridge close to Cologne, Germany will be discussed. Multiple sensors (thermocouples, strain gages and embedded ultrasonic sensors) were installed in the bridge. The bridge will be monitored for at least one month and subjected to a load test. As a result, the influence of temperature should be detected and quantified and a diagnosis of the bridge condition (especially changes in cracks) should be established.
Monitoring of Bonding Quality in CFRP Composite Laminates by Measurements of Local Vibration Nonlinearity
In this paper, an approach based on measurements of a local nonlinear response of the laminate is suggested and applied to monitoring contaminations of adhesive bonding in carbon fiber-reinforced polymer (CFRP). It is shown that a contaminated boundary layer of the adhesive contributes to an overall nonlinear response of the laminate that enables to evaluate and quantify bonding quality caused by various types and levels of single contaminations. All kinds of contaminations studied in the context of aviation applications result in enhancement of the nonlinear response of the CFRP laminate which is an indication of deterioration of bonding quality. The enhancement of nonlinearity in weaker bonds was also applied to imaging of the contaminated adhesion areas in realistic airplane components.
Nonlinear Ultrasonic Wave Spectroscopy for Detecting Local Defect Resonance Frequency in Delaminated GLARE Plate
The structural health monitoring (SHM) technology has received wide acceptance in recent times, where the load-bearing capacity of any structure is a paramount concern. The detection of damage present in the composite structure is quite crucial for better functioning and prolonged service life of a structure. The SHM method based on nonlinear wave spectroscopy (NWS) is a reliable technique for determining the presence of defects in composite structures. The defects in the form of delamination present in composite laminates can be detected by exciting the structure at a critical frequency known as local defect resonance (LDR) frequency of the defect. The excitation of any structure with its LDR frequency leads to an abrupt rise in amplitude of vibration at the defect location and thus leading to high contrast imaging of the defect area. In this paper, a wave propagation study based on explicit dynamic analysis is performed to solve the reverse problem of SHM in thin GLARE plate due to low-velocity impact damages. Moreover, a signal processing technique based on the Fast Fourier Transform (FFT) and bicoherence analysis is implemented for detection of LDR frequency. Two different delamination configurations are considered for single as well as dual excitation cases, in order to prove the efficiency of bicoherence tool in detection of multiple delaminations at different excitations. The LDR frequency obtained from bicoherence analysis is found to be in good agreement with LDR frequencies calculated analytically as well as those obtained from FFT plots. The bicoherence analysis is found to be a reliable and robust tool for detecting LDR frequency and can be used for high contrast defect imaging.
Modeling and Experiments on Nonlinear Ultrasonic Guided Waves for Fatigue Crack Detection and Evaluation
This paper reports the recent modeling and experimental endeavors on nonlinear ultrasonic guided waves for fatigue crack detection and evaluation in the Active Materials and Intelligent Structures (AMIS) lab at Shanghai Jiao Tong University. The progress on efficient modeling of Contact Acoustic Nonlinearity (CAN) using Local Interaction Simulation Approach (LISA) is first presented. The modeling results will illustrate the mechanism of higher harmonic generation as well as special feature related to the rough crack surface condition. Subsequently, the experimental investigations will be presented, striving to adopt the aforementioned nonlinear phenomena for fatigue crack detection and evaluation. The first experimental research effort aims at incorporating the wave control capability of metamaterials into the nonlinear ultrasonic SHM and NDE procedure. An ultrasonic bandgap meta-surface will be demonstrated for the elimination of inherent second harmonic to enhance the sensitivity and reliability of the nonlinear ultrasonic system. The second experimental effort adopts the nonlinear mode conversion and scattering features of guided waves at fatigue cracks for improved imaging of the damage using the Scanning Laser Doppler Vibrometry (SLDV). These research results show that nonlinear ultrasonic guided waves enable the effective detection and evaluation of fatigue cracks. The paper finishes with summary, concluding remarks, and suggestions for future work.
Thermal Wave Imaging for Defect Determination During Automated Fibre Placement
Due to recent advances in automated fibre placement (AFP) methods there is a demand for non-destructive evaluation of fibres during structural fabrication. A thermal wave imaging technique is proposed for the evaluation of defects in the AFP process, such as misaligned fibres, debonding between layers and broken fibres. The method focuses on the evaluation of thermal images taken with infrared cameras during the AFP fibre placement procedure. Thermal wave imaging determines defects/damages by assessing the uniformity of heat dissipating through materials. This work looks at the potential of such a system to accurately map the AFP process one layer at a time and build a threedimensional defect/damage analysis of the final structure, which would provide potential for automated algorithms to locate areas of misaligned fibres, layer debonding or fibre breaks. Defects were evaluated by capturing thermal images in real-time after fibre placement, this was done by evaluating a simulated AFP process, by adding additional layers to a test sample. By parametrically assessing any changes in results between successively captured areas and layers it was possible to identify defects. An X-Y gantry system was used to provide evidence for the concept in terms of real-time adaptability within the production process of structures, yielding layer by layer information of the built structure, while also providing information about part to part repeatability of the process.
Outlier Analysis of Nonlinear Solitary Waves for Health Monitoring Applications
Structural health monitoring methods based on highly nonlinear solitary waves are emerging as a potential cost-effective technique to monitor or inspect a variety of structures. In the present study, the use of outlier analysis in the form of a discordancy test was investigated to enhance the damage detection capability of an HNSWs-based monitoring system. An experiment was conducted to detect simulated defects in a thick steel plate. HNSWs features were extracted and fed to a univariate analysis that compared the testing data to a set of baseline data. The results show that the outlier analysis improves the HNSWs ability to detect damage.
Ultrasonic Guided-Wave Based System Identification for Beams
Structural health monitoring (SHM) usually requires several stages of information, starting from damage detection, localisation, and identification. Ultrasonic guided waves can travel long distances with relatively low attenuation, which enables them to interact with any potential damage present in the structure. This paper focuses on the use of a novel ultrasonic guided wave propagation model in order to provide both damage localisation and identification. The wave propagation model used here is a state of the art method for transient simulation of ultrasonic guided waves in one dimensional structures both isotropic and anisotropic. This is embedded in a framework for generating excitation signals and capturing scattered signals from damage at any point in the structure. The methodology computes the complete transient response at a fraction of computational cost of full finite element (FE) method. Two kind of damages are modelled: (1) a transverse crack and (2) a delamination in composite beams. To address damage identification and quantification, a model based Bayesian inverse problem is formulated so that both damage scenarios are identifiable. The proposed methodology is exemplified in a beam using the case study of a simulated delamination between two layers. The results show that the proposed framework classify and localise the damage accurately.
Assessing the Likelihood of Damage at the Start of a Structural Health Monitoring Campaign
In many structures that engineers are interested in monitoring the damage state of, the infeasibility of collecting data in each possible damage condition makes unsupervised learning algorithms an appealing approach. However, this learning method generally relies on access to an initial data set being representative of the undamaged behaviour of the structure, which often is not available, particularly when a monitoring system has been retrofitted to an operational structure. To overcome this issue, arbitrarily selecting data over an initial period of monitoring to represent the undamaged/normal condition is a common method to adopt, however, there are a variety of limitations with this approach, such as uncertainty in the damage condition of the structure prior to the installation of the monitoring system, in addition to the length of the required training period. It is therefore desirable to be able to infer a structure’s damage condition at the start of a monitoring period, explored here through the application of the Dirichlet Process Gaussian Mixture Model, a clustering method based on a Bayesian adaptation of the traditional Gaussian Mixture Model. In the context of condition assessment for wind turbine components, this paper combines the application of prior knowledge and population-based SHM in conjunction with this clustering method to provide a framework to assess the likelihood of damage at the start of a monitoring campaign.
Experimental Validation of the Population-Form to Represent Nominally-Identical Systems
As a framework for population-based Structural Health Monitoring (SHM), a Gaussian processes (GP) is suggested to represent a population of systems. Specifically, a Gaussian processes is used to model the population-form, trained using data collected from a simulated population of nominally-identical structures. The form is then used for damage detection through comparisons to future measurements. Significantly, the GP regression, used to represent the form, generalises to experimental data, which were not (directly) used to train the GP.
Bayesian Damage Characterization Based on Perturbation Model Utilizing Responses at Vibration Nodes
Most of the Structural Health Monitoring (SHM) methods are struggling among the number of sensors, the accuracy in damage detection and the requirement of a model. This paper explores the possibility of using a Bayesian probabilistic approach for damage imaging utilizing dynamic response at a few vibration nodes. In addition, the computational model in the Bayesian framework is replaced with a surrogate model based on perturbative solutions. This paper tries to propose a vibration-based SHM method which is suitable for real-time monitoring, requires a small number of industrial sensors, not rely on a high-fidelity FE model and can be applied for damage assessment of location and severity. In the present paper, several case studies demonstrate the advantage and feasibility of the proposed method.
Output-only Identification of Loading and Modal Parameters in Dynamic Systems with Non-Gaussian Inputs
There exist engineering systems of interest which are not subjected to loads which can be modelled as a white Gaussian noise. A pertinent example of this is in the offshore energy industry where structures will be subjected to narrowband loading from the fluid structure interaction with the waves. This time-varying narrowband loading can make usual output-only identifications methods, e.g. stochastic subspace identification, difficult to implement effectively. The determination of modal parameters of a structure is a mainstay of modern SHM analyses, however, additional benefit can be found from recovery of the time history of the loading signal. This paper demonstrates the use of a Bayesian state-space formulation to address this problem. Rather than modelling the loads on the structure as a white-noise process, the loading is considered an additional latent state of the system. Additionally, that latent state is assumed to be a sample from a Gaussian process in time with a known stationary covariance function. In this way, a state-space representation of the Gaussian process governing the forcing is recovered. This approach enables the time history of loading on the structure to be recovered with quantification of uncertainty. The effectiveness of this method is demonstrated on a representative three-storey building structure.
On an Application of Probabilistic Risk Assessment to Structural Health Monitoring
A key motivation for implementing structural health monitoring is to facilitate decisionmaking regarding the operation of a structure throughout its life. The notion of risk has been used to inform decision-making under uncertainty in industries such as nuclear energy and aerospace - formalised in a procedure known as probabilistic risk assessment. The current paper aims to exploit methods used in probabilistic risk assessment to demonstrate a novel risk-based approach to structural health monitoring. The approach utilises a probabilistic graphical model framework in which information is passed from a probabilistic classifier to an influence diagram representing a decision-process via a Bayesian network representation of a fault tree. The risk-based approach is demonstrated on simulated data from a finite element model of a four bay truss.
Bayesian Information Fusion for Fatigue Crack Growth Diagnosis using Ultrasonic Guided Wave Pitch-catch in a Piezoelectric Actuator-sensor Network
Diagnosis of fatigue crack growth in thin metallic structures using guided ultrasonic waves has been shown to be a promising methodology for aerospace and structural engineering applications. Typically, the non-destructive evaluation (NDE) tests are performed using the pitch-catch mode, where Lamb waves are induced in the metallic structure using a PZT actuator, and the wave field at a suitable location on the structure is measured using a PZT sensor. The NDE methods employ a model-based, baselinereference approach, where any changes in the sensed signal are assumed to be caused by fatigue crack growth. These NDE methods, however, are prone to inaccuracies due to aleatory uncertainty in system properties as well as physics model discrepancy. Probabilistic treatment of the diagnosis faces two key challenges: a large parameter space, and computationally expensive numerical models of the governing physics. In this work, we discuss a probabilistic crack diagnosis framework to overcome the aforementioned challenges; and to tackle the aleatory and epistemic uncertainties in the process. We build a Bayesian network that describes a Lamb-wave pitch-catch NDE method using a low-fidelity, physics-based model of the same. We perform global sensitivity analysis to quantify the contribution of various parameters to the variance of the damage-sensitive output signal feature(s) using this model. We retain the parameters with higher contribution, and build a medium-fidelity, one-way coupled, multi-physics finite element model to simulate the piezoelectric effect and Lamb wave propagation. We use the finite element model to generate training data to train a Gaussian process (GP) surrogate model that can output the damage-sensitive signal feature for given values of pertinent system parameters. We use the GP surrogate to perform Bayesian diagnosis of crack size considering data corrupted by measurement and process noise. We fuse the information obtained from various actuator-sensor paths in a PZT network to sequentially update the crack growth estimate. The proposed Bayesian estimation and fusion approach can potentially improve the performance of Lamb-wave pitch-catch NDE methods for metallic structures.
Risk-Adaptive Maneuver for Maintenance-Free Operation
This paper introduces an operational construct called Risk-Adaptive Maneuver (RAM) for extending the maintenance-free operating period of future aircrafts. The overarching goal is to establish an integrated operation-sustainment (OS) paradigm that enables continuous aircraft operation with minimum maintenance and logistical support. We define RAM as an integrated OS construct in which maneuver profiles are optimally selected to maximize or extend the maintenance-free operating period of an aircraft without exceeding the operational risk threshold. RAM deals with the following questions: Given a desired (or a must have) incremental MFOP of ΔtMFOP in flight hours or miles, what is the optimal set of flight regimes that the aircraft can execute such that the probability of surviving ΔtMFOP is greater than some acceptable threshold? The paper presents efforts undertaken to answer this and related question.
Probabilistic Damage Quantification via the Integration of Non- parametric Time-Series and Gaussian Process Regression Models
One of the major challenges the structural health monitoring (SHM) community is facing today is related to enabling accurate and robust damage detection and quantification under varying operating and environmental conditions with a limited number of sensing data sets. In particular, state-of-the-art active-sensing SHM technologies face significant difficulties in accurately detecting and quantifying damage within uncertain environments due to the lack of appropriate statistical learning and inference methods. As such, it is critical for the community to have available tools that accurately detect and quantify damage, in a probabilistic sense, using the –oftentimes limited– available information. In this study, a novel probabilistic damage detection and quantification approach is proposed based on the integration of non-parametric statistical time series representations and Gaussian Process Regression Models (GPRMs). Initially, non-parametric models based on Short-Time Fourier Transform (STFT) power spectral density (PSD) estimates are used in order to detect damage and statistically determine signal (wave) paths that carry the most information about damage size. Next, GPRMs are trained using Damage Index (DI) values from selected wave propagation paths and used to estimate the damage size based on the current DI sets. The experimental assessment is presented for data recorded from a notched aluminum plate. It is shown that, using wave propagation paths selected by non-parametric statistical models, estimation accuracy increases and damage size estimation confidence levels become narrower, thus providing a robust and efficient damage detection and quantification approach.
A Decision Support System for Scour Management of Road and Railway Bridges Based on Bayesian Networks
Flood-induced scour is the excavation of material around bridge foundations due to the erosive action of flowing water and it is by far the leading cause of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fail every year whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Monitoring an entire infrastructure network against scour is not economically feasible. A way to overcome this limitation is to install scour monitoring systems (SMSs) at critical bridge locations, and then extend information gained to the entire asset through a probabilistic approach. In this paper, we propose a Decision Support System (DSS) for road and railway bridge scour management that exploits information from a limited number of scour monitoring systems to achieve a more confined estimate of the scour risk for a bridge network. A Bayesian network (BN) is used to describe conditional dependencies among the random variables involved. It allows estimating the present and future scour depth distribution using real-time information from monitoring of scour depth and river flow characteristics. Data collected by SMSs and BN’s outcomes are then used to inform a decision model and thus support transport agencies’ decision frameworks. The idea is to use this information to update the scour threshold after which bridges are closed. A case study consisting of several road bridges in Scotland is built to demonstrate the functioning of the DDS. They cross the same river and only one of them is instrumented with a SMS. The BN is found to estimate accurately the scour depth at unmonitored bridges and the decision model provides higher values of scour threshold compared to the ones implicitly chosen by transport agencies.
Identification of Active and Passive Sources Using Elastic Waves and Computational Intelligence
The application of structural health monitoring is determined by the received sensor data and the interaction between structure and environment. Due to this interaction, the raw signal will always include scattering and noise, which has to be processed and filtered efficiently. In this study, computational intelligence is used to recognize patterns in the sensor signal. To train the system and predict the location precisely, numerical simulations and physical experiments of various scenarios are performed in order to create a large database. Once the training process with numerical data is completed, the trained system is applied to new numerical and experimental data and will predict the impact and damage location. The result proves that computational intelligence is a promising method to localize arbitrary sources in real applications. Furthermore, the proposed work can be easily applied for various damage cases and structures and is supported by the strongly growing field of computational intelligence and big data analysis.
Intelligent Damage Detection of Composite Structure Based on Convolutional Neural Network and Wavelet Transform
Carbon fiber reinforced plastics (CFRP) composites has been widely used in aerospace, automotive and other fields due to its superior comprehensive performance. To guard against possible risks, nondestructive testing (NDT), such as Lamb wave has been used to monitor the internal damage of the CFRP composites structure and develop the dependable structural health monitoring techniques. However, since the high degree of anisotropy in the CFRP composites makes the failure mode under the fatigue load complicated, an accurate and timely damage detection of the CFRP composites is still difficult. This paper proposes a new damage detection method using convolutional neural network and continuous Wavelet transform. An experimental study is implemented to verify the effectiveness of the proposed method. The result shows that the proposed methodology is a useful technique for precise detection of delamination damage in the composites structure.
The Unidirectional Excitation of Guided Waves with an Increased Power Using Multiple Transducer Arrays in Pipelines for Structural Health Monitoring
Ultrasonic guided waves with features can be used for non-destructive testing (NDT) and structural health monitoring (SHM) on large-scale engineering structures, such as plates, pipelines, and rails. In this paper, an array of transducers with multiple rings for excitation scheme is proposed to enhance the energy of unidirectional guided waves for defect detection on cylindrical structures. The Gaussian-windowed pulse as an incident signal is designed based on the method of elastic waves superposition. The numerical simulations in ABAQUS are modeled to generate unidirectionally a pure torsional wave mode T(0,1) at the center frequency of 35 kHz using an increased number of rings of transducers on a steel pipe. The predictive results are validated experimentally. The performance of guided wave testing is evaluated using two and three rings of transducers for inspecting a 2% cross-sectional area (CSA) defect on an 8-inch schedule 40 steel pipe. Results that show guided waves as an active-excitation method can be developing for SHM of steel pipelines. Based on the method of the unidirectional enhancement of guided wave technique excitation, there are many potential applications for developing guided wave technique in other large-scale structures for SHM.
Location Specific Temperature Compensation of Guided Wave Signals Applied to Pipe Inspections
The baseline subtraction method is widely used to detect defect signatures in guided wave structural health monitoring. In essence, an earlier measurement is subtracted from the ‘current’ signal, and high residuals might indicate damage occurrence. However, varying environmental and operational conditions, such as temperature, also produce signal changes and hence, potentially, high residuals. A number of temperature compensation methods have been developed, which typically targets the varying wave speed due to varying temperature. Nevertheless, other, subtler effects caused by temperature variations are often overlooked, such as changes in attenuation, in the transducer frequency response and in the relative amplitudes of different modes excited by the transducer. A novel temperature compensation method has been recently presented by the authors, which compensates for temperature induced changes of wave speed and signal phase. The compensated signals can then be fed to a second temperature compensation procedure that has been newly developed. This will correct any spatially dependent signal change that is a systematic function of temperature, hence producing residuals less affected by temperature variations. This new method was applied to a set of T(0,1) guided wave signals collected by a pipe monitoring system, yielding residuals reduced by at least 50% compared to those obtained using the standard approach at positions away from structural features, and by more than 90% at features such as the pipe end. The method therefore promises a substantial improvement in the detectability of small defects, particularly at existing pipe features.
Single Sensor Localization and Characterization of Acoustic Emission Sources in Metallic Panels: A Deep Learning Approach
This paper presents data-driven localizing and characterizing of acoustic emission (AE) sources in metallic panels. Deep learning is used to leverage the multimodal and dispersive reverberations of such sources in real-world panels with geometric features, such as rivet connections, doublers, and stringers. The deep networks used in this study are stacked autoencoders trained on the wavelet transform and frequency spectrum of AE waveforms. To evaluate the performance of this deep learning-based approach, Hsu-Nielsen pencil lead break sources were generated on a real-world Boeing-777 fuselage panel. The results show that the proposed approach can localize and characterize AE sources with only one sensor placed in the far field.
Spatial Integration of Baseline-free Damage Detection Algorithms Based on Dual-PZT for the Structural Health Monitoring of Anisotropic Composite Aeronautic Structures
The focus is put here on the Structural Health Monitoring (SHM) of composite aeronautic structure using Lamb waves emitted and recorded with piezoelectric transducers (PZT). Conventional algorithms perform Lamb waves acquisition in the healthy state of the structure (referred to as the “baselineâ€) and then compare incoming data from an unknown state with this one to detect, locate, classify and quantify any potential damage. The acquisition, storage, and update of the initially recorded baseline database constitute a severe drawback of such algorithms. Indeed, the structure under study as well as the environment may vary during its operational life without the appearance of any damage and thus the initial baseline may not be relevant at any instant where damage monitoring is needed. In order to circumvent this drawback, “baselinefreeâ€ method (such as the instantaneous baseline [BI] and rupture of reciprocity [RR]) have been developed. Moreover, the use of dual-PZT, i.e. concentric PZT made of a ring and a disk lying on the same ceramic, has been shown as attractive for baselinefree purposes. However, now that several algorithms based on dual-PZT are available, no study dealing with the spatial integration of the results provided by these algorithms have been reported in the literature. It is thus proposed in this paper to investigate strategies for the spatial integration of common baseline-free methods (namely BI and RR) on an experimental case of damage on a highly anisotropic composite plate. Results illustrate the decomposition of Lamb wave modes in signals measured via dual PZTs as well as the proposed spatial integration strategies for these methods.
Entropy-Based Damage Evaluation of Composite Structure and Its Application to Structural Health Management
Assessment of material state damage in a composite structure using fundamental thermodynamic concept of entropy has been proposed in this article. The health management system of composite structure is mainly based on installation of piezoelectric material on the structure itself. To supplement this existing system, in this article, it has been demonstrated that the change in entropy due to material degradation can be used as a measure of damage. The advantage of this entropy-based damage index measurement is that it offers a unified and more comprehensive indication of damage irrespective of the path of the damage mode. Since the dissipation of energy due to structural loading initiates damage from an ideal material-state, an estimation of material state disorganization from an unloaded-state can be considered as a change in entropy. Over the years, many attempts had been made to implement this concept in metallic structure, however, the application of this approach to composite structure is far from trivial. To circumvent this gap, an entropic characterization of fatigue degradation mechanism under various loading rates has been proposed and demonstrated for carbon fiber based composite structure using material state property information. Essentially, an experimental setup was designed to assess the change in structural integrity of the composite specimens at multiple fatigue cycles using the stateof- the-art scanning acoustic microscope. The statistical mechanics-based probability distribution function for the change in structural properties are then expressed in terms of fundamental entropy distribution and a damage index of the structure is formulated. Next, the specimens went through a time dependent stress relief cycle, and the entropy as a thermodynamic state function for the damage was assessed by using the change in probability distributions. Based on initial results, the proposed methodology provides enough proof to effectively identify the damage initiation irrespective of its type and the localization of the damage
Learning Guided Wave Dispersion Curves from Multi-Path Reflections with Compressive Sensing
Guided wave dispersion curves characterize materials and the waves that propagate in them. While there have been many efforts to learn dispersion curves from data, multipath reflections cause most methods to either fail or require a significant quantity of data. This paper uses compressive sensing and sparse recovery algorithms to learn dispersion curves from known multi-path characteristics. We assume that reflection signals originate from virtual sources, according to the method of mirrors. We demonstrate our methodology with guided wave simulation data of a 1.125m by 1.00 m convex, nonsquare aluminum plate. We show that our multi-path method can accurately estimate dispersion curves with as few as 20 measurements, whereas more than 100 measurements would be necessary without it.
The Characterization of Weld Morphology Using Ultrasonics
Welding is a complex manufacturing process, which involves significant physical and chemical changes in materials. Meanwhile, unexpected defects such as lack of penetration, undercutting, crater crack, burn-through and porosity may occur during manufacturing process. Therefore, the assessment of weld quality by means of nondestructive testing is needed. In this paper, the weld morphology is investigated by means of linear and nonlinear ultrasonics. The relationship between weld morphology and ultrasonic features (signal energy ratio extracted from linear ultrasonics and acoustic nonlinearity coefficient extracted from nonlinear ultrasonics) are built. Different penetration morphologies (bead, shallow penetration, excessive penetration, and burn-through) show different ultrasonic signatures. The microscopic observation is used to build the numerical models that validate the experimental measurements. The experimental and numerical results show that linear ultrasonics is influenced by the penetration morphology (shape, width and depth). The acoustic nonlinearity coefficient is influenced not only by the weld morphology but also heterogeneities (e.g. porosity) in the medium. The experimental and numerical results are used to build the regression models such that the ultrasonic features can be exploited to assess the weld morphology in situ without any destructive testing.
Nonlinear Seismic Response Reconstruction in Minimally Instrumented Buildings—Validation Using NEESWood Capstone Full-Scale Tests
The authors present a methodology to reconstruct nonlinear seismic response and assess seismic performance of instrumented buildings subjected to earthquakes. The paper proposes the use of a nonlinear model-based state observer (NMBO) that combines global acceleration measurements and a nonlinear model of the building to estimate the complete dynamic response including displacements, velocity, acceleration and internal forces in all structural members. The proposed NMBO is designed to be realizable as a modified structural model of the building. The modified model consists of the original nonlinear model of the building with added grounded dampers placed at the accelerometer locations. The modified model is driven by corrective forces applied at the accelerometer locations and defined as the measured velocity response scaled by viscosity of the grounded dampers. The optimal viscosity of the added dampers is found by solving a matrix equation that yields the optimal dampers to minimize the estimation error. The response of the modified system to the corrective forces constitutes an estimate of the true response of the building. The fact that the proposed observer is realizable as a modified structural model makes it compatible and easy to implement in the environment of commercial structural analysis software and therefore amenable to implementation by practicing engineers. From the estimated dynamic response, engineering demand pa- rameters, such as inter-story drifts are obtained and compared with performance-based acceptance criteria to determine if the building meets the desired performance objective. The methodology is validated using seismic response measurements from the 2009 NEESWood Capstone full-scale tests.
An Intelligent Wireless System for Real-time Seismic Monitoring of Civil Infrastructure
Traditional seismic monitoring systems for civil infrastructure using wired sensors can be prohibitive, due to onerous and expensive cabling networks. Wireless smart sensors (WSS) offer tremendous opportunity to reduce cost and realize the promise of pervasive sensing. However, seismic structural monitoring using WSS remains elusive, and main obstacles include stringent energy constraint and limited transmission bandwidth. To address the challenges, an intelligent wireless system is developed as a cost-effective solution of real-time seismic monitoring of civil infrastructure. The system consists of three main components: ultralow-power on-demand sensing prototypes, a high-throughput live streaming framework, and a MATLAB-based application for real-time damage assessment. The capabilities of the developed system are validated by a lab test, demonstrating that the system can detect seismic motions, provide high-quality synchronized data, and present damage assessment in real-time.
Comparison of Indicators of Damage Location Based on Information Gain
Vibration-based methods for damage localizations are often based on a damage feature defined in terms of changes of modal or operational shapes. These methods allow detecting variations of the damage feature that can be attributed to damage. Many of these methods are based on the detection of irregularities in the modal shapes of the structure. In this paper, the performance of several algorithms for damage localization is investigated and the results are compared both qualitatively, in terms of the capability to correctly localize damage, and quantitatively, in terms of relative information entropy. This parameter quantifies the gain of information obtained a given damage indicators with respect to a reference one. As reference damage indicator is assumed the prior probability of damage defined in terms of expert opinion or as a non-informative parameter. The investigation is carried out using responses simulated using the calibrated finite element model of a real Italian bridge permanently monitored by the Italian Seismic Observatory of Structures.
Estimating Damage Level in Reinforced Concrete Structures Based on the Detection of High-frequency Transients
The current paper discusses a specific processing technique to be applied on structural response data during an earthquake event for the purpose of damage estimation. In essence the processing technique is a damage detection procedure which relies on detection of high-frequency transients generated during an earthquake response of an instrumented reinforced concrete structure. A damage indicator is mathematically formulated under the assumptions that the high-frequency transients are related to damage progression and that damage stops to accumulate at the deformation peaks of the structure. Finally, the entire procedure is demonstrated and validated with the help of response data from a shaking table test of a reinforced concrete column.
Seismic Monitoring of Cable-stayed Bridge Using Wireless Sensor Network
This paper describes an ongoing research work on the seismic monitoring of cablestayed bridge using wireless sensor network. Newly developed wireless sensor network that is based on simultaneous high-speed flooding technique is found efficient in transmitting the packets at the same timing without causing synchronization to the carrier frequency or phase. The wireless network monitoring system was implemented on the Shin-Nakagawa Bridge, 530-m single-plane single-pylon steel-box girder cablestayed bridge in Ibaraki prefecture in Japan. The structure is seismically isolated with rubber bearings and damper at the main pylon and piers. A total of 27 sensor nodes were installed on the cable-stayed bridge and viaduct at several locations on the pier, main pylon, girder and on the ground. The monitoring system has been in place since the summer 2017 and successfully recorded structural responses from more than twelve small-to-moderate scale earthquakes. This paper explains the wireless sensor system, monitoring system and analysis of recorded earthquakes.
A Non-parametric Approach Toward Structural Health Monitoring for Processing Big Data Collected from the Sensor Network
Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information poses many challenges. This paper presents a machine learning algorithm for processing of big data collected from the sensor networks of civil structure. The proposed approach consists of training and monitoring phases. The training phase was focused on the extracting statistical features and conducting Moving Kernel Principal Component Analysis (MKPCA) in order to derive the damage sensitive indices. The monitoring phase included tracking of errors associated with the derived models. The main goal was to analyze the efficiency of the developed system for health monitoring of the benchmark experimental data with the 17 different damage scenarios. In this paper KPCA has been implemented in a new form as Moving KPCA (MKPCA) for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that, the proposed health monitoring system has a satisfactory performance for the detection of the damage scenarios in three-story frame aluminum structure. Furthermore, enhanced version of KPCA methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods.
Seismic and Structural Health Monitoring Systems for Large Concrete Dams
This paper is focused on the LNEC experience regarding the development and operation of continuous vibrations monitoring systems in large concrete dams. The goal is to emphasize the importance of the combined use of monitoring data and numerical models for Seismic and Structural Health Monitoring (SSHM) of Dams. The SSHM system installed in Cabril dam (132 m high), in operation since 2008, is described. The design and implementation of this system, including hardware and software, was the result of a long-term LNEC research program, still on-going. The SSHM system software was developed in order to automatically perform the analysis of collected data, including the automatic comparison with numerical results from 3DFE models. Similar systems have been installed in other large dams in Portugal.
Seismic Structural Health Monitoring of a Six-story Wooden Building
The Building Research Institute and the Japan 2x4 Home Builders Association jointly built a full-scale six-story wooden experimental building. The building was densely equipped with two types of accelerometers to investigate its dynamic characteristics in detail. The fundamental natural frequencies and damping ratios were identified using the strong motion data from more than 100 earthquakes. Identified results widely varied affected by several factors. As one of the factors, the seasonal fluctuation in the dynamic characteristics was recognized by the system identification using ambient vibration data. The fluctuation can be explained as the influence of temperature and relative humidity. The dependence of the dynamic characteristics on response amplitude could be observed in the analytical result using strong motion data as well. Finally, the soil-structure interaction effect was examined from the strong motion data using a simplified rocking-swaying model. As a result, the considerable soilstructure interaction effect was recognized.
Identification of Sparse Damage in Steel-Frame Buildings Using Dense Seismic Array Measurements
There is an unprecedented increase in the number of real-time measurements produced by permanent, dense accelerometer arrays in buildings, an example being the Community Seismic Network. In the present work, damage identification techniques are developed by coupling such datasets with linear and nonlinear finite-element models of buildings. Damage in steel-frame buildings is manifested in localized areas as cracks in beam-column connections or as an average stiffness reduction. High-fidelity linear or nonlinear finite-element models are developed to allow for realistic behavior, including modeling nonlinearities associated with the opening and closing of cracks. L1 regularization techniques and sparse Bayesian learning tools are further developed fully in the time domain to reduce ill-conditioning and account for the sparsity of damage. The effectiveness of the proposed methods in identifying the location and severity of damage is demonstrated using simulated acceleration data from a three-story steel frame building, and a 15-story building in downtown Los Angeles that is fully instrumented.
Seismic Damage Identification for Bridges Based on Transmissibility Function and Support Vector Machine
Vibration-based damage identification and structural health monitoring have been developed in recent years. However, it is still a challenge to effectively extract the information of structural damage from a large amount of monitoring data and to realize the rapid detecting damages and assessing structural conditions of bridge structures under seismic excitation. To address this problem, transmissibility function (TF) is used in this paper, and based on the seismic monitoring data of bridge structures, an intelligent algorithm integrating transmissibility function and support vector machine (SVM) is developed for multi-level/scale structural damage identification under seismic excitation. Firstly, the TF is calculated with the measured structural vibration responses. Then, the indicator takes the sum of transmissibility along the specific frequency range is used as input for SVM. Finally, the classification algorithm of support vector machine is employed to structural damage alarming under earthquake. The proposed method could eliminate the influence of external excitations because transmissibility function only depends on the measured seismic responses of structures and the information of external excitations is not required. Further, the proposed method is sensitive to the structural damage. To illustrate the performances and feasibility of the proposed algorithm, the damage identification of numerical simulation under seismic excitation is studied.
In Situ Distributed Strain Monitoring of a Small-Scale Reinforced Concrete Structure Using Combined FBG/OFDR Fiber Optics Sensing for Assessment of Its Seismic Degradation Mechanisms
An experimental program has been defined by EDF (French electric utility) and CEA with the aim to improve the knowledge of the wall-slab junctions’ behavior, by means of reduced-scale mock-ups made of reinforced concrete (RC), representative of Nuclear Power Plant (NPP) building structural elements. As part of the Tamaris platform (CEA), the "Azalée" shaking table provides 3D excitation. A wall-slab ¼- scaled mock-up was designed, constructed, equipped with both Optical Fiber Sensors (OFS) and conventional instrumentation (accelerometers, displacement & strain gauges, Digital Image Correlation (DIC)) and vibration-tested. OFS provide in-situ local or distributed strain measurements, complementary to other methods. Gathering in situ distributed strain data is thus of considerable interest since it helps reducing experimental variance and eventually improving modeling accuracy. Fiber Bragg Grating (FBG) and Optical Frequency-Domain Reflectometry (OFDR) sensing techniques are well suited to Structural Health Monitoring (SHM) of RC structures during seismic evaluations. As a proof-of-concept, sensing rebars were embedded within the wall-slab mockup. Artificial ground motion test sequences were applied producing cyclic bending. Wavelength/frequency data were recorded by FBG/OFDR units during several runs with accelerations growing up from 0.18 g up to 2 g. Dynamic strain, natural frequencies, and both local (Bragg) and distributed (OFDR) permanent strains were then compared to both modeling and complementary data. OFS sensing reveals itself as a helpful investigation method for Structural Health Monitoring (SHM) of RC structures, in order to assess for seismic degradation mechanisms. To the authors’ knowledge, this experiment is a world first involving both in situ FBG and OFDR measurements simultaneously in this context.
Recognition of the Distress in Concrete Pavement Using Deep Learning Based on GPR Image
Road testing results are the basement of road maintenance decision-making. However traditional drilling core damage the structural integrity of the pavement, and work period is also disadvantage in the maintenance of the road. Ground penetrating radar (GPR) which is non-destructive and high efficiency, has outstanding advantages and become the most important way to check the internal distress of the concrete pavement, but the subject remains a challenge in the acquired radar signal interpretation, especially under noisy interference the traditional methods is difficult to recognize the distress accurately. Based on the current research status of the concrete pavement inspection, we proposed GPR pavement distress automatic recognition technology based on the deep learning. Through simulation technology, the various types of distress in the inspection of pavement concrete using GPR was simulated such as void, disengaging and loose. Based on GPR data collection, the features of the concrete pavement is analyzed. The deep learning models was established for recognizing distress concrete pavement structure. The establishment of the sample was from simulation results of concrete pavement, then the datasets were built. Recurrent neural networks (RNN), convolution neural networks (CNN) and deep belief networks (DBN) were compared in the study, and the advantages of automatic deep belief networks is shown in recognizing distress. Finally, stacking automatic coding machine was applied to practical engineering. Based on experiment, the proposed method accurately recognize the concrete pavement distress, and the type of the distress also classified.
Monitoring Cracks with Nonlinear Coda Wave Interferometry (NCWI): Numerical and Experimental Results
In this paper, the Nonlinear Coda Wave Interferometry (NCWI) method is studied for an overall assessment of cracking levels in very heterogeneous materials. NCWI is an ultrasonic method that mixes the performance of coda wave interferometry, which takes advantage of the heterogeneity of the medium to detect small changes, and nonlinear acoustics that displays high sensitivity to cracks. First, a series of experimental results are summarized. They include laboratory measurements on crack concrete samples as well as on perforated aluminium plates prepared so as to mimic cracks growth, and on-site measurements on a reduced 1:3 scale concrete nuclear containment plant (EDF VeRCoRs mock-up) under loading conditions that replicates decennial inspection. All experimental results demonstrate that the NCWI observables are sensitive to crack length (even when closed by post-tensioning) and can thus be used to monitor crack status in very heterogeneous material. Even though the experiments show a clear relation between crack length and crack opening, until very recently it was not possible to quantitatively recover information about the cracks as no model exists that can predict the NCWI experimental results. To tackle this problem the Spectral Element Method (SEM) is used to perform parametric numerical sensitivity studies of the NCWI observable to crack status. The aim here is to find simple and robust laws that links the NCWI observables to quantitative information about cracks (here the total crack length or the viscoelastic properties of an equivalent damaged zone). In this paper specific attention is given to the confrontation between numerical and experimental data obtained in perforated aluminium plates. A linear law between a CWI observable and the crack length is validated. Finally, perspective of an imagery NCWI based method is discussed.
Prediction of Volumetric Strain in Concrete Due to ASR Reactions Using Acoustic Emission Technique and Artificial Neural Network
An alkali-silica reaction (ASR) is a swelling reaction that occurs over time in concrete between the alkaline in cement paste and the reactive silica found in many common aggregates used to make concrete. The swelling reaction causes the initiation and propagation of cracks in concrete structures, leading to their deterioration and eventual failure. Development of a reliable structural health monitoring method for detecting crack initiation and propagation, and evaluating concrete condition is important for assessing the integrity of structures. In this paper, we investigate the correlation between acoustic emission (AE) data and volumetric strain for concrete during ASR and develop an artificial neural network (ANN)-based model to predict volumetric strain using AE signals. The AE data and volumetric strain are collected from three concrete specimens. Two of the specimens contain reactive aggregates that accelerate the ASR reactions, while one specimen serves as a controlled specimen without ASR reactions. AE events of all three specimens are monitored using an array of piezoelectric sensors, and the volumetric strain of each specimen is measured monthly. The AE data collected from the sensors are then preprocessed to obtain damage sensitive features, such as count, amplitude, and signal strength. Using the preprocessed data, two back-propagation feedforward networks are trained to predict volumetric strain using either cumulative number of counts only or both cumulative number of counts and the number of counts per hour as inputs, respectively. The results show that the trained neural networks provide good prediction accuracy for the volumetric strains of concrete with ASR reactions after the volumetric strain exceeds 0.036%.
Towards Embedded Fiber Optics for the Ultrasonic Monitoring of Concrete
The most widespread sensors embedded in concrete aim to monitor temperature and strain. They are mainly providing local information at the position of the sensors themselves. In recent years new embedded sensors in concrete have been considered by various research groups in view of providing quantitative information in volume so that damage location does not have to be known precisely prior to sensors installation. For instance some piezoelectric ultrasonic sensors are currently being tested in real bridges to monitor damage and/or cracks. Embedded sensors based on electromagnetic waves or electric fields are also under development to monitor water and/or chloride ingress. All those promising ongoing works show that, thanks to embedded sensors, it will soon be possible to monitor in 2D or 3D on site concrete structures. In this paper we show preliminary experimental results using embedded Fiber Bragg Grating (FBG) in concrete to record ultrasonic signals at several locations along a single optical fiber. The advantage of FBG for concrete monitoring is obvious: the footprint of the optical fiber itself is very limited compared to other sensors cables, its durability in harsh conditions such as salty environment or radioactive one is a real advantage, in addition it is a broad band transducer which allows multiple scale analysis. After a description of the FBG sensing principle and the optical fiber installation into decimeter and metric concrete samples, ultrasonic signals recorded with a spacing of 5 cm will be shown and analyzed. The sensitivity of the FBG will be commented with regards to a selection of piezoelectric ultrasonic sources covering body waves as well as coda waves frequency range (typically between 50kHz to 500kHz).
A New Thermal Integrity Method for Pile Anomaly Detection
Anomaly detection is a hot topic in pile construction which is a complex one due to the intrinsic nature of underground structures, such as limited accessibility, large depth and complex soil profile. Several traditional pile integrity tests have been developed before, which, however, cannot give detailed information of anomaly size, nature or precise location. This paper proposes a new thermal integrity testing method that employs distributed temperature sensors and combines concrete heat hydration numerical model. The fundamental principle is that early-age concrete releases heat during curing. If defects such as voids, necking, bulging and/or soil intrusion exist inside the concrete body, they will result in local temperature variations. The developed numerical model uses Finite Element Method (FEM) to simulate concrete hydration and heat transfer. The model can be customised for different concrete mixes, pile geometries and built-in anomalies. The modelling results are then compared to field test temperature data. Any temperature disparities indicate potential anomalies of the pile structure. The proposed new test will reduce the cost and increase the accuracy compared to traditional integrity tests. It will be a promising method for the future deep foundation assessment.
Ultrasonic-Acoustic Emission Hybrid System for Monitoring Concrete Structures Affected by Alkali-silica Reaction
Alkali-silica reaction (ASR) in concrete can cause internal cracks resulting in structure deterioration. Current ASR assessment and monitoring programs still rely on visual inspection of the concrete surface cracks or measuring the concrete expansion. These methods cannot provide a comprehensive assessment or the mechanical information needed for a lifetime prediction. In this work a hybrid health monitoring system using active ultrasonic testing and acoustic emission testing for monitoring ASR damage in concrete is proposed. The two systems share the same sensors and work independently without mutual interference. This system was deployed on three large concrete beams: control, ASR unconfined and ASR 2D-confined samples. The relative velocity change is extracted from the ultrasonic signals, and have a good agreement with the AE results, number of cumulative hits. These results are also confirmed by the volumetric expansions. The initiation of rapid alkali-silica reaction inside the two ASR samples is detected by the ultrasonic method and expansion measurement and is confirmed by the AE results. The proposed hybrid monitoring system can provide a comprehensive solution for long-term monitoring of ASR damage in concrete.
Fabrication of Self-Sensing Carbon Fiber Cementitious Composites: Best Practices
Self-sensing cementitious composites are ideal for use as sensors in structural health monitoring of concrete structures because of their similar physical and mechanical properties to those of the host structure and their proven strain sensitivity. A conductive phase (such as carbon fibers, carbon nanotubes, carbon black, etc.) is typically incorporated into cement paste to obtain cementitious sensors. Previous research has mainly focused on proof-of-concept demonstrations of the strain-sensing properties of cementitious sensors. The sensitivity, repeatability, and stability of carbon fiber reinforced cementitious (CFRC) sensors are significantly influenced by the mix design and the quality of fiber dispersion. So far, fiber volume fractions and dispersion techniques have been specific to each research project. The main objective of this study is to provide a basis for developing a standardized mix design and fabrication procedure for CFRC sensors. First, the mix design and fabrication procedures used in previous research were reviewed and compared to identify the optimum fiber content and the most efficient balance of mechanical and chemical fiber dispersion techniques. The effect of a partial replacement of cement by supplementary cementitious materials was also examined in terms of the target rheological and workability properties. The second part of the study presents the results from a preliminary experimental investigation for evaluating the microstructure of CFRC sensors. Sensor samples were prepared by adopting the best fabrication practices identified in the first part of the study. The fiber dispersion quality was evaluated via several image processing techniques using combined reflection and transition optical and scanning electron microscopes.
Rapid Condition Evaluation of Concrete Pavement with Automated Acoustic Scanning System
The paper describes a practical and rapid automatic crack evaluation system (ACES). It is a newly developed automated multichannel rapid automatic impact system for rapid evaluation of bridge decks based on impact echo test using a rolling hammers impactor and air-coupled sensing with high spatial resolution providing high impact power for deep delamination and consistent scan resolution regardless of speed. The researchers have developed rapid ACES approach using a high-speed stress wave generator to show pavement deficiencies. The guided wave were used to identify internal and surface vertical cracks. The resonance vibrations by generating stress waves (called as impact echo) are used to identify the internal horizontal crack and its delamination. The RPI mechanical waves produce a vibrational resonance through the thickness of the pavement, which can be detected by using non-contact micro-electro-mechanical system (MEMs) sensors for high sensitivity and high speed data collection[1]. The acquired signals will be transferred and stored in a computer with software that can develop signal processing and create identification algorithms. Its results identified the peak frequencies on a short time Fourier transform (STFT) spectrogram in the frequency range 0.5–6 kHz. Both results were verified on a concrete slab with artificial delaminations in the laboratory. The results were compared to both the air-coupled manual impact echo approach and 3D ultrasonic tomography (MIRA) results. A positioning system is integrated to automatically track test positions and save time in documenting and identifying test results and position information. Rapid condition evaluation of concrete runway pavement helped the strategic evaluation and implementation of a proper pavement maintenance and replacement plan.
Health State Estimation of Hydraulic System Based on Generalized Multiclass Support Vector Machine
Health status estimation plays an important role in hydraulic system for ensuring safety and efficient operation. Accurate health status estimation can effectively avoid sudden failure of hydraulic system and greatly reduce maintenance costs. Support vector machine (SVM) is a common and effective method for health status estimation. However, traditional extensions of the binary support vector machine (SVM) to multistate classification are either heuristics or require solving a large dual optimization problem. This paper presents a generalized multiclass support vector machine (GenSVM)-based method for health status estimation of hydraulic system. First of all, the multisensory monitoring signals are preprocessed and sensor data which are highly correlated with health status are selected. Then, feature extraction is implemented using massive multiple sensor signals. Meanwhile, a new and flexible GenSVM model is constructed to identify the health status of hydraulic system, which integrates simplex encoding for input data high-dimensional transformation. In addition, grid search is used to determine the optimal model parameters. A practical application case study is implemented to verify the effectiveness of the proposed method. Health state estimation for the four main components of the hydraulic system is achieved including cooler, valve, pump and accumulator. The results show the superiority performance of the proposed method compared with other standard methods.
Development and Application of High Temperature Coke Drums Structure Health Monitoring System
The coke drum, work under high temperature, coke in and out with hot and cold distress through the cycle of 495℃ to 50℃, is the core reactor of the delayed coking device. Typical failure modes of bulge deformation of tower, low cycle fatigue crack of weld, material aging, the tower body bending and inclining is easy to be caused under the comprehensive impact of complex load condition, temperature stress and mechanical stress of coke drums, which is a serious threat to safety production. As the requirements for long period running of the refining enterprise continue to improve, the problem of inconsistencies and incongruity of the traditional periodic inspection period and equipment maintenance cycle is becoming more and more prominent. How to meet the stable operation, long period running and ensure the safety and steady operation of the equipment is a great problem. Aiming at the typical failure modes of coke drums during operation, a health monitoring system for high temperature coke drum structure is designed and developed in this paper, which integrates strain, crack, vibration, temperature and other monitoring means. To analyze and calculate the fatigue damage degree, an improved random load spectrum rain flow counting analysis method is proposed, and a damage model based on memory degradation miner criterion is also established. In addition, good results have been achieved with the system applied on the coke drum of a large petrochemical enterprise. The abnormal conditions such as active defects, bending and inclination of the tower body have been found in time during operation, and the healthy state of the coke drum structure has also been diagnosed based on the method proposed in this paper. The system developed in this paper lays a technical foundation for further developing of damage trend forecasting and predictive maintenance for key parts of coke drum structure, and effectively guarantees long-term safe, healthy and stable operation of coke drums.
Application of Acoustic Emission Technology on SHM of Overhead Travelling Cranes and Portal Bridge Cranes
The steel structures of overhead travelling cranes and portal bridge cranes are voluminous and complex, making it difficult to quickly test the safety and integrity. Acoustic emission (AE) technology is an effective tool for Structure Health Monitoring of cranes. AE characteristics of the steel structures of cranes, including attenuation curves, signal characteristics of crack and deformation, were obtained in lab studies. Combined with a large number of field tests, the AE testing standard of cranes was developed. The filed application of an overhead travelling crane was shown and the testing results were evaluated using the ISO standard.
Structural Health Monitoring and Safety Evaluation of Pressure Vessel by the Use of Acoustic Emission Technology
Acoustic emission technique is a widely used nondestructive method for pressure vessel testing and monitoring. Since it was applied to pressure vessels in the early 1980s in China, more than 2000 fields PVs has been tested and monitored by China special equipment inspection and research institute. Based on the monitoring results, the acoustic emission sources that could be encountered during monitoring of pressure vessels are presented, which are crack initiation and propagation, weld defects cracking, residual stress releasing, oxide scales peeling off, structural friction, leakage, electronic noise, wind blasting and et al. The characteristics of each acoustic emission source are summarized respectively. The safety evaluation method based on AE monitoring for pressure vessel is given. The evaluation of acoustic emission sources is according to the activity and intensity of acoustic emission source. Acoustic emission source activities are divided into 4 levels including in weak, medium, strong and very strong. Acoustic emission source intensities are divided into 3 levels including in low, medium and high. The final evaluation of acoustic emission sources are given in 4 levels by colligating activity level and intensity level of acoustic emission sources. In addition, two typical application of acoustic emission technique on PV monitoring are introduced.
Rolling Element Bearing Fault Diagnosis Using Compressed Sensing and Convolutional Neural Network
Rolling bearing is one of the most commonly used components in rotating machinery. It's so easy to be damaged that it can cause mechanical fault. Therefore, it is of great significance for its condition monitoring and fault diagnosis. However, the traditional diagnosis methods still suffer from two problems, which are (1) the information density of the monitoring data is low because of huge monitoring data amount, and (2) the requirements of domain expertise and prior knowledge for sensitive feature extraction. Aiming at above problems, a new diagnosis method based on compressed sensing (CS) and convolution neural network (CNN) is proposed in this paper. The method consists of three key steps. First, the original monitoring signals are converted into compressed sensing domain for reducing data amount and improving its information density by using compressed sensing method. Second, the compressed signal is used as the input of the convolution neural network to extract sensitive features adaptively, and to realize the fault intelligence diagnosis. Finally, several groups of experiments are carried out to validate the feasibility of the proposed method in this paper, and the diagnostic accuracy achieves 93.7%, which is far higher than the traditional methods.
Development of a Shipboard Wireless Monitoring System to Monitor Ship Crews during Extreme Blast Load Exposure
Naval ships can be exposed to munition detonations during their operations in theaters of engagement. Munition blasts can be devastating to the ship structure as well as to the crew within the ship. Current understanding of the impact of blast loads on ships is based on empirical evidence from operational events that have inflicted casualties and injured sailors; there are comparatively fewer examples of controlled blast experiments on actual ships. Controlled blast testing on ships could advance understanding of how blast pressures propagate within the ship interior and the impact of these loads on ship crews. This study focuses on the development of a self-sufficient wireless monitoring system that can be used to monitor ship structures and the response of ship crews during blast events. The proposed system is designed to be deployed on decommissioned, crewless naval ships used as targets during naval exercises. The system consists of two parts. The first is a stationary shipboard monitoring system installed on the ship to monitor the ship motions and to capture ship vibrations and internal pressures during a blast event. The stationary monitoring system is powered entirely by solar energy and includes a GPS receiver to monitor ship locations and a satellite modem for the communication of data. The stationary system includes wireless sensors installed on the ship structure that trigger to capture ship vibrations and internal compartment pressures just prior to and during the blast event. The second part of the shipboard monitoring system are mannequins instrumented with wireless sensors to measure and transmit mannequin motion during blast loading. This paper describes the design of the monitoring solution and its deployment on a decommissioned ship exposed to blast loads during naval exercises. The result from sea trials reveal a stable and self-sufficient monitoring system that is survivable to the blast event.
Recognition Method of Health Monitoring Point of Quayside Container Crane Based on Fuzzy Evaluation
The quayside container crane is the crucial equipment of port handling operation. With the increasing demand of trade logistics, quayside container cranes are developing towards more and more large and high-parameter to adapt to larger ships. With the increase of logistics demand, quayside container cranes are developing towards large-scale and high-parameter. Nevertheless, the fatigue problem of metal structures has become increasingly prominent due to the long-term and high-strength continuous operation of equipment. The structure of quayside container crane is huge, and there are many types of connection, such as welds, openings, variable crosssection, etc. Therefore, the mechanical condition of equipment structure is complex. How to effectively determine high-risk fragile zones and health monitoring points is an urgent problem to be solved, which is also the key to success or failure of structural health monitoring. Five key factors of quayside container crane is been considering, such as structure type, welding technology, heat treatment, non-destructive testing and load condition, this paper puts forward a method for identifying health monitoring points of quayside container crane. Firstly, the hierarchical structure model of highrisk fragile zone fuzzy recognition is constructed. Secondly, the analytic hierarchy process (AHP) is proposed to determine the weight of each index. Then, the risk value of fragile zone is calculated by using the theory of fuzzy evaluation. Finally, the method is used to identify the high-risk fragile areas and health monitoring points of quayside container cranes. In view of the field application of health monitoring for a port quayside container crane, the method proposed in this paper is used to determine the location of health monitoring points, which coincides with the location of cracks in equipment in the past. The method proposed in this paper provides a scientific and effective means for the location selection of structural health monitoring, and has important value for improving the effectiveness of structural health monitoring.
Monitoring of Wind Turbine Blades Using Radar Technology: Results from a Field Study at a 2MW Wind Turbine
Millimeter-wave and terahertz technology have been used successfully in nondestructive testing (NDT) applications for material defects identification. Such systems were recently proposed for structural health monitoring (SHM) to identify defects online during operation. One interesting application field is the monitoring of wind turbine blades that have large dimensions and a heterogeneous material system. Millimeterwaves are able to penetrate the dielectric components of the blade and sensitively interact with material defects such as cracks, delaminations and debondings. This paper presents results from a field study at a 2 MW wind turbine. The focus of this work is on the analysis of radar signatures at different parameters of the wind turbine such as variations of the azimuthal position of the nacelle. Therefore, consecutive radar signals are processed in a dedicated way to identify relative changes between different rotor blades. In addition, a methodology will be presented on how to apply an artificial damage on the surface of the rotor blade. Therefore, a remotecontrolled car (called Blade Rover) was modified so that it could spray shaving foam onto the rotor blade at a defined location. The shaving foam serves here as a reversible damage model, because it has a significant dielectric contrast due to its water content.
Structural Health Monitoring: A Review of Uncertainty Quantification Methods in Wind Turbine Systems
Wind energy has been in the forefront of renewable energy sources and is expected to show continuous expansion in the future. Nevertheless, there is a considerable demand for the development of new technologies as the turbines get more complicated, bigger and requirements for design, operation and maintenance become stricter. Uncertainty quantification (UQ) can contribute to further technology development, it is important to analyse both data and model results to understand limitation of current modelling, data variability and take informed decisions because uncertainties are present in all domains of wind turbine design, modelling, manufacturing and maintenance. To have adequate UQ, it is important to consider the sources of the uncertainty, which can generally be either aleatory or epistemic. Composite material with complicated microstructures have become crucial to wind turbine systems and so have their behaviour characterisation, thus requiring UQ knowledge for both the material properties and damage models. Different models and data entail ( or are available to) different UQ methodologies. With the intensification of non linear and multiphysics modelling, computational cost has also become a decisive aspect of uncertainty quantification. This article discusses the sources of uncertainties in wind turbine blade models, reviewing the main techniques used to propagate uncertainties within wind turbine systems.
Fibre Reinforced Polymer Structure Under Simultaneous Temperature and Humidity Influence—FBG Sensors, THz Spectroscopy, and Vibrothermography Methods
Fibre reinforced polymers are commonly used in many industrial branches. The technical progress in applied science and technology requires advanced materials. The desire to reduce the structure weight and its manufacturing costs may affect the safety and reliability of designed structure. Safety and reliability requirements result in the development of a variety of structural health monitoring systems and non-destructive testing techniques. The paper presents a comparative study of three methods (FBG sensors, THz spectroscopy and thermography) to study their sensitivity and applicability limitations with reference to the simultaneous influence of temperature and relative humidity.
Gaussian Processes for Structural Health Monitoring of Wind Turbine Blades
Nonstationary environmental and operational variables (EOVs) acting on wind turbines present challenges for the successful application of structural health monitoring systems. A contributing factor to these challenges is the fact that EOVs are often not monitored sufficiently, leading to uncertainties being introduced into monitored components. The method proposed in this paper takes advantage of the fact that all blades on a wind turbine possess nominally identical properties and encounter the same EOVs. Gaussian processes (GPs) are used to learn the relationships in the properties between pairs of blades when they are in a healthy state. The GPs then predict the properties of one blade, given that of another, and deviations between the actual and predicted properties (i.e. the residual errors) are used to indicate the occurrence of damage. To validate this method, it is applied to data from a real wind turbine, where some form of blade damage has been known to have taken place. X-bar control chart analysis shows that this method identifies damage as early as six months before the damage led to problems.
Structural Strain Monitoring of a Composite Scaled Turbine Blade Using Embedded QRS Sensoring
The growing development of renewable energies is leading to the fabrication of composite parts that will be subjected to more severe environment, such as the case of tidal and offshore wind turbine blades. The maintenance cost of these composite parts is into their operational costs (OPEX) and impacts directly the energy cost generated by them. The condition based monitoring and structural health monitoring are compulsory to reduce the OPEX and make these renewable energies more attractive. One of the difficulties of monitoring such structures is for instance their operation in harsh environment, therefore impacting the reliability of the sensing and measurement system. This paper addresses the utilization of embedded Quantum Resistive Sensors (QRS) for strain and damage monitoring into composite structures during operation. Such kind of sensor is twofold interesting, since it can be embedded in the structure, it is not exposed to harsh environment and also it can measure strains inside the composite structure without weakening it. The QRS is made with the addition of carbon nanotubes in an epoxy resin (which can be the same as the matrix of the composite) and can be embedded in the core of the composite structure during its fabrication; this embedment process is also addressed here. Thanks to their very low thickness (less than 10 μm), QRS are almost not intrusive when embedded and avoids degradation of structure’s mechanical properties, and creation of new defects [1]. The work presented here is part of a French R&D project called EVEREST [3], where a scaled tidal turbine blade was manufactured in carbon composite with QRS embedded sensors and mechanically tested for structural monitoring [2, 3]. The results are encouraging and showed that a resistance drifting of the QRS happens during structural loading, which is related to damage initiation in the composite structure. This behavior can be exploited as “smart materialâ€ and is correlated to the damage level of the sensor itself and the composite structure, and therefore employable in SHM. The measurement results and application of QRS is assessed experimentally and discussed in this article.
Structural Health Monitoring, SHM, as a Tool for Optimizing the Performance of a Wind Power Plant
Main task of an SHM system on a wind turbine is to detect defective developments at an early stage on all load-bearing components such as rotor blades or tower. This prevents damage and allows repair measures to be planned in time. But a vibrationbased SHM system can do much more! For example it can be used to detect incorrect pitch angle settings or imbalances and to monitor the loads acting on the system. Of course, additional sensors and algorithms are required for such additional functions of an SHM-System. The lecture reports on several years of experiments on a commercially operated wind turbine Nordex N117 with 2.4 MW nominal power provided with more than 100 additional sensors. This turbine is used as a research system. In detail, all 3 rotor blades were equipped with accelerometers, strain gauges, FBGs and gyroscopes. The nacelle was instrumented with temperature, acceleration and speed sensors at the fast shaft and the tower and foundation were provided with strain gauges and inclination sensors. For population monitoring, a wind farm with 8 turbines of the same type N117, also with a rated output of 2.4 MW per turbine, was used. However, no additional sensors were installed on these wind turbines. Instead, the existing ice detection system IDD.Blade®, which is also based on acceleration measurements, was used. In addition, the SCADA data was analyzed. For the tests concerning mass imbalance, additional weights were fixed to the rotor blade, for the tests concerning aerodynamic imbalance, the pitch angles were shifted manually. For all other developments, the time-synchronous data recorded over several months was analyzed. The necessary algorithms for automated data analysis could be developed on the basis of the tests and the long-term measurement data. In addition, the dynamics of the wind turbines was investigated with simulations.
Flexible Electronics and Software for Stretchable Sensor Networks in Structural Health Monitoring
This paper presents the design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for a stretchable sensor network. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. This paper also describes all accompanying software to program the system chips and display all sensor data. These electronics were mounted directly onto a flexible PCB frame that surround the fully stretched sensor network. The primary purpose of this system is to convert the sensor signals into meaningful data, which is displayed live for an end-user to view and analyze. This paper discusses the challenges and accomplishments in the development of this new system including (1) specifying signal conditioning circuitry and components for all three sensor types (2) simultaneous sampling for PZT sensors for impact detection (3) Proper placement of IC's and other components for optimal hardware design and (4) configuration of firmware/software for correct system operation. The paper will also discuss possible optimizations for this design.
Laser Cut and Chemical Release Screen Printing Approaches for Creating Substrate Sparse Sensor Networks
Today’s SHM technology is based on either rigid or flexible intrusive substrates which increase weight, disrupt function and lead to decreased efficiency in component operation. Separating a sensor system from its original substrate is useful for many applications, including aerospace, automotive, robotics, and industrial manufacturing. This work outlines physical and chemical methods for screen printing substrate sparse sensor networks. The physical method (Laser Cut) uses a green femtosecond laser to ablate away substrate in all non-critical areas, while the chemical method (Chemical Release) dissolves all or part of the substrate to leave free-standing inks. The Laser Cut network features 17 sensor nodes, including commercially-available lead zirconate titanate (PZT), resistance temperature detector (RTD), and strain gauge sensors. The system was attached to a demonstration processing frame, with frame and network then mounted to a cylindrical surface to demonstrate the network’s capacity for conformal monitoring. Chemical release network experiments were focused on rapid screening of the most promising substrate and ink combination pairs to determine the next steps for improving the maturity of the technology. The concept fits the aviation industry ‘power-by-the- hour’ business model which leads to operational efficiency, increased safety, cost savings and additional business for original equipment manufacturers and operators.
Fabrication and Functionalization of Distributed and Stretchable Sensor Networks for Structural Health Monitoring
Structural health monitoring (SHM) technology takes advantage of distributed sensors to detect the damage and assess the state of a structure. Owing to the development of modern CMOS and MEMS technology, millions of miniaturized sensors can be microfabricated at an unprecedented speed and scale. However, a standard process for manufacturing multimodal sensors in a platform and then integrating them into a large-sized structural component still requires further development. In this article, we describe process improvements to produce stretchable sensor networks with higher signal-to-noise ratios and higher measurement accuracy using conventional microfabrication techniques. Specifically, two improvements were accomplished: (1) aluminum was used to replace gold for significant cost and resistance reduction; (2) a shielding layer was added to mitigate the effects of electro-magnetic interference. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. In addition, the network was handled with an in-house developed stretch machine to automatically expand it to cover a desired area. The released and stretched network is laminated into a flexible frame with edge-mount electronics for signal conditioning, processing, power and wireless communication capabilities. To demonstrate the business impact of the sensor network, a small composite wing will be created and the sensor network will be integrated into the wing for measurements of impact, strain and temperature. Collectively, the measurement results represent a robust and reliable sensing system that is able to generate superior state sensing and awareness capabilities for SHM-based applications.
A Robust Expansion Process Technology for Deployment of Stretchable Sensor Networks
The development of smart structures in the context of aerospace, automotive, and infrastructure engineering is a grand engineering challenge that has received increased interest in recent years [1-3]. With this increased interest comes a growing need for sensor networks that can be permanently integrated into the structural elements of buildings, vehicles, and infrastructure, providing health monitoring and state awareness to these elements while in service. The Structures and Composites Lab at Stanford University has developed a sensor network architecture that is capable of stretching from a 100mm wafer to cover a 1m2 area in temperature, impact, and strain sensors, and is permanently integrable within laminated structures [4]. In this paper, automated stretching and deployment of a structural “dummyâ€ of this network over a designed 2-D area is performed and examined for the first time.
Design of Island-Bridge Layout Stretchable Electronics for High Spatial Accuracy Deployment
Stretchable electronics composed of island-bridge layouts are being utilized in numerous research thrusts such as structural health monitoring, energy harvesting and storage, and wearables. Based on this topology, current study presents a design approach aimed at design of stretchable electronics devices to a high spatial accuracy. In our approach, we represent the island-bridge layout device using a surrogate linear spring - rigid node model for high-efficiency simulations. Linear springs are given stiffness and stretchability parameters based on possible serpentine interconnect variations, which are characterized through an automated multi-level optimum design space search and analysis study. Starting with generatively producing a family of designs, we determine the stretchability and stiffness of each design configuration via finite element analysis. Finally, the choice of each interconnect between nodes is determined by posing the objective island locations, local and global constraints, and possible interconnect configurations as a constraint satisfaction problem (CSP) and solving via AC-3 algorithm. We demonstrate the results of our approach via a set of computational simulations in which a non-uniform grid stretchable sensor network is designed. This benchmark study demonstrates the potential of our technique in achieving high accuracy in sensor deployment, as well as the reproducibility of the process
Dynamic Characteristics of Vehicle-Track System Affected by Cement Asphalt Mortar Gap
The interface damage between slab track and cement asphalt (CA) mortar is one of the common damages of CRTS II slab track. It is therefore of great engineering significance to study the influence of CA mortar gap on the dynamic response of vehicle-track system. Firstly, a vehicle-track coupling dynamic model considering CA mortar gap is proposed on the basis of the vehicle-track coupling dynamics principle. Secondly, the length, width and height of the CA mortar gap are selected as the key influence parameters. The orthogonal test method is used to design the experiment, and the simulation analysis is carried out according to the experimental design. Finally, the parameter sensitivity analysis of the dynamic response of the wheel-rail system is carried out by the range analysis method. The results show that the influence of CA mortar gap on the dynamic characteristics of vehicle system is small, while the influence of CA mortar gap on the dynamic characteristics of slab track is large. From the sensitivity analysis, the width of CA mortar gap has the largest effect on the dynamic response of vehicle-track system, followed by the length and height of CA mortar gap. In the daily maintenance of slab track, the expansion of the width of CA mortar gap should be strictly controlled.
Propagation Characteristics of Ultrasonic Guided Waves in High-Speed Rails
With the rapid development of the high-speed railway, the continuous welded rail (CWR) has been widely used, and the ultrasonic guided waves become particularly suitable for the non-destructive testing of the rails due to their advantages. In this paper, a three-dimensional finite element modelling of rails is proposed on the basis of rail material and geometric properties. The first N-order natural frequencies of the rail are obtained by using the characteristic frequency method, and the phase velocity dispersion curves of the rail in the frequency range of 0-5000Hz are then provided. The narrow-band signal modulated by Hanning window is selected as the excitation signal source of ultrasonic guided waves, and the propagation characteristics of the low frequency ultrasonic guided wave in the rail are investigated. The results show that the energy is mainly concentrated in the rail flange, and less energy is distributed in the rail head and the rail web, which indicates that the defects in the rail flange could be relatively easily detected by using a low frequency guided wave.
Using Solitary Waves to Monitor/Measure Axial Stress
This article presents a numerical formulation about the dynamic interaction between highly nonlinear solitary waves generated along a mono-periodic array of spherical particles and rails in a point contact with the array. A general finite element model of stress-free and axially stressed rails was developed and coupled to a discrete particle model able to predict the propagation of the solitary waves along an L-shaped granular medium located perpendicular and in a point contact with the web of the rail. The models were validated experimentally by testing a 0.9 m long rail segment subjected to compressive load. The scope of the study was the development of a new nondestructive evaluation technique able to estimate the stress in continuous welded rails and eventually to infer the track neutral temperature, i.e. the temperature at which the longitudinal stress in rails is zero. The numerical findings presented in this article demonstrate that certain features, such as amplitude and time of flight, of the solitary waves are affected by the axial stress. The experimental results confirmed the numerical prediction although laboratory constraints limited the ability to extend the stress range to significant levels of compression or to test longer rail segments in the operating rail system.
The Study of Running Stability of Piggyback Car Based on a FE-Rigid Coupled Model
The piggyback car not only had the flexible and convenient characteristics of highway transportation, but also had the advantage of railway transportation, such as the capacity, safety and reliability of heavy haul transportation. “Truck on carâ€ was a transportation mode, the truck mass has been simply assumed to be the increment of car body mass so far, but recent study on actual railway carbody experiment reveals that the truck mass cannot be assumed as the mere increment in carbody mass, for it was a kind of dynamics affecting carbody vibration. In this study, two type models are carried out to present the investigation into the influence of the truck on car for the random response of the system. The PRAP was effectively applied to simplify the calculation, in which 6 points of the system were chosen as estimation locations, significant difference random response was found between the two model on the direction of lateral, it also could be noticed that the stability index of carbody depends on the truck on carbody dynamics clearly. The stability index of coupled model was 19% more than the other one on the lateral. The coupling vehicle random vibration simulation method could be used for accurate calculation of the further improvement of the structure, it can provide a new thinking for stationarity evaluation of this type freight car.
Monitoring-Assisted Derailment Prediction of a High-Speed Train Running on a Long-Span Cable-Stayed Bridge
In this study, a novel approach considering the dynamic interaction of trainbridge system based on the Vector Form Intrinsic Finite Element (VFIFE) method is proposed for evaluating the risk of derailment of a train traveling over a long-span cable-stayed bridge under crosswinds. Making use of the VFIFE method, the train and bridge systems can be discretized into a group of mass particles with massless stiffness elements and each mass particle satisfies the Newton’s second law. The internal forces induced by pure deformations in the massless elements are calculated using the fictitious reverse motion method, by which the conventional updating, factorizing and inverse procedures for solving structural matrices of the train-bridge system are skipped. As a result, the equation of each mass particle can be solved individually. For evaluating potential derailment of a running train under crosswinds, the running safety factors including derailment factor, offload factor and lateral wheel–rail force have been obtained. In the case study, a two-phase plot with safety and derailment regions is configured for a train running on a cable-stayed bridge under crosswinds. Results show that the wind-induced vibration of the system may affect significantly the running safety of the train. When the time-varying parameters required are available from online and onboard monitoring systems, the proposed model can be executed to assess the derailment risk in a real-time manner.
Variational Autoencoder-Based Approach for Rail Defect Identification
Nowadays, machine learning has been widely used in all fields of scientific research, and structural health monitoring (SHM) is no exception. However, for structural health monitoring, damage-relevant data is difficult to obtain in many cases. That is to say, an important branch of machine learning, supervised learning, is difficult to be applied for structural damage identification. On the other hand, generative models, a mechanism for unsupervised learning tasks, can be exploited to structure condition assessment, and autoencoder along with variational Bayes is an effective approach for implementation. This investigation aims to formulate an acoustic emission (AE)-based method for rail damage detection by using the properties of latent space under the autoencoding framework. The AE-based detection system is enabled by four piezoelectric ceramic transducer (PZT) sensors symmetrically attached to both sides of the railroad turnout. The collected signals are incorporated into variational autoencoder (VAE) [1] organization as the input, and the latent variables that extract from original information during encoding procedure are operated as the core indicators for the condition assessment of rail track. Signals collected from intact railway turnout and that containing damage information are both used in this investigation and they present different clusters. This result demonstrates the high potential of the latent space under the variational Bayesian framework for identification of rail defects.
An Inversed Greedy Method for Information-Based Optimal Sensor Placement on Bridges
An information-based optimal sensor placement (OSP) strategy is explored in the context of bridge health monitoring. An inversed greedy algorithm is proposed for optimizing the sensor placement since the conventional greedy algorithm can only achieve a near-optimal solution with large uncertainty. This study intends to reconstruct complete modal shape configuration based on the deployed sensors by employing Gaussian process regression (GPR) method, where the modal components from the deployed sensors are utilized for searching the optimal positions on the bridge. First of all, the GPR method is exploited to establish a probability model of the unsensed positions based on the data from the deployed sensors. Then, to maximize the information that the probability model contains, the mutual information is employed as the objective function and interpreted by the proposed inversed greedy algorithm. The performance of the proposed method is verified through a numerical case study, where the conventional greedy algorithm and the genetic algorithm are also implemented for the purpose of comparison with the proposed algorithm. The results show that the inversed greedy algorithm outperforms the greedy algorithm and the genetic algorithm since it can provide a solution which is closer to the optimum in terms of the prediction error.
Acoustic Emission Feature Extraction and Classification for Rail Crack Monitoring
Crack monitoring of rails aims to identify fatigue cracks in advance in order to ensure a safe and smooth operation of railway system. This study focuses on the rail crack monitoring using acoustic emission (AE) technique in the railway field typically with complex crack conditions and high operational noise. There are mainly three types of AE waves respectively induced by operational noise, crack propagation and impact, which need to be carefully distinguished from each other for the sake of quantitative crack monitoring. Wavelet transform (WT) was applied to represent the features of AE waves in the time-frequency domain. AE waves induced by different mechanisms were found to contain various instantaneous frequency components. A deep convolutional neural network (CNN) with transfer learning was proposed as an automatic feature extractor and classifier to evaluate the WT plots of AE waves. The CNN was trained, validated and tested using AE data collected through field and laboratory tests. The results demonstrated that the proposed methodology performed well in classifying AE waves induced by different mechanisms in the railway field.
Monitoring of Rail Bolted Joint Looseness with PZT Network- Based EMI Technique Under a Deep Learning Framework
Rail bolted joints (RBJs) are vital components served as either fastening rails or connecting adjacent rail sections in rail systems. In the era of high-speed rail (HSR), bolted joint fasteners are the most common types to keep rail tracks in position; although continuously welded rails (CWR) are mainly used in HSR lines for safety and ride comfort concern under high-speed operation, insulated rail joints (IRJ) are still widely utilized in low-speed zones (e.g., railway stations). Loosening of RBJs has potential risk to failure of circuit insulation, track displacement and misalignment, and subsequently even derailment. The monitoring of loosening of RBJs is therefore highly desired to maintain safe operation. The electromechanical impedance (EMI) is a promising ultrasonic structural health monitoring technique for structural deficiency or damage diagnosis. This paper introduces a novel EMI method for real-time monitoring of loosening of RBJs by employing a network of piezoelectric lead zirconate titanate (PZT) transducers pre-implemented on the host structure. The embedded PZT network and the host structure form a coupled system, and the coupled EMI which is highly sensitive to localized structural changes is measured to indicate the loosening of bolted joints. Convolutional neural networks (CNN) are applied to automatically extract key features from the joint EMI data and quantify the preloading forces on the bolted joints. While some damage metrics cannot well reflect the state of the RBJ, the proposed model can effectively and precisely predict the looseness of the bolted joint and outperform the model that utilizes only the data from one sensor.
A Bayesian Probabilistic Approach for Damage Detection of a Population of Nominally Identical Structures: Application to Railway Wheel Condition Assessment
This paper proposes a Bayesian probabilistic approach to deal with the damage detection of a population of nominally identical structures. In this approach, a probabilistic reference model is first established with sparse Bayesian learning to describe structural dynamic characteristics of all nominally identical healthy structures using structural health monitoring data. Then, the conditions of the rest of structures can be identified through the examination of discrepancies between the new monitoring data and model predictions. To formulate the damage detection in a more scientific way, the discrepancies are examined by means of Bayesian hypothesis testing that allows to qualitatively and quantitatively evaluate structural conditions. To validate the feasibility and effectiveness of the proposed approach, its application to railway wheel condition assessment is presented with the use of online monitoring data collected by an optical fiber sensing track-side monitoring system.
Application of Driving Recorder to Evaluate Rail Irregularity and Vehicle Swing
Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail condition efficiently and accurately. In this paper, a new technique based on driving recorder and image analysis is proposed to evaluate the rail condition. In line with the videos of driving recorder, three coordinate systems for train, rail, and ‘observer’ are defined in the Lagrangian space, respectively. The relationship between the three coordinate systems is then figured out to facilitate the identification of rail condition such as rail curvature, rail irregularity, and rail vehicle swing, etc. Based on the identified rail condition status by the proposed technique, the assessment of derailment, damage at rail fasteners, and contact condition between the wheel and rail can be conducted. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique. The results show that the proposed technique is competent to evaluate the rail curvature, rail irregularity, vehicle swing, and train running speed accurately. Moreover, the location where the contact between the wheel and rail is abnormal can be identified by the proposed technique. It is testified that the proposed technique provides a simple and accurate way to inspect the rail condition.
3D Displacement Monitoring of Railroad Bridges Using Unmanned Aerial Vehicles (UAVs)
Railroad network is the main freight transportation systems in the US, with 40 percent of the total freight traffic being transported by rail. More than 50% of the railroad bridges in North America are over 100 years old. Nowadays, train loadings on bridges are higher than what they were designed to be in the past. Railroad bridges have been capable to adapt to new loads and successfully ensure safe operations by proper maintenance and repair since their inception, including continuous monitoring to proactively prevent any disruption in the transportation. Railroad managers have indicated that measuring the transverse displacement of the bridges under train crossing events can be used to quantify their proper performance and to inform management or repair decisions with objective data. Traditionally, Linear variable differential transformer (LVDTs) and other types of sensors are used to obtain the displacement data of the train, but these methods need human forces for their installation. Additionally, some parts of the bridges are not accessible or easy to reach for these types of inspections. Thus, aerial and remote sensing have become more popular. However, current methods for using Unmanned Aerial Vehicles (UAVs) to monitoring railroad bridges rely on cameras or lasers which are either technologically complex or economically unaffordable at large scale. This paper explains the development of a new hybrid sensing method to obtain total dynamic displacement using UAV. Sensors collecting displacements include lasers, cameras, and dynamic 3D cloud data acquisition. Displacement of the bridge is collected with the use of a low-cost laser sensor on the UAS with a camera attached to it. Then, the new algorithm extracts the hovering of the UAS and correct the laser displacement to obtain the displacement of the bridge under load. The results are compared to LVDT displacement for validation. The paper also identifies the priorities of North American Railroad owners regarding how the proposed technology can be implemented for new cost-effective management of railroad bridges maintenance.
Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Normalized Impact-Echo Method
The severe deterioration of the CA mortar layer may lead to the movement of the upper concrete slab and compromise the safety of the speedy train. In this study, a specimen simulating the structure of high-speed rail track slabs was built with delaminated cracks embedded inside the CA mortar layer. In the specimen, the defects are located at either top concrete slab-CA mortar interface or CA mortar-bottom concrete slab interface. The lateral size of the defect and mortar-thickness are also experimental variables. The impact-echo tests were performed above the defected and good-bond locations. Normalized amplitude spectra were obtained by dividing amplitude of the impact-echo spectrum to the amplitude of the spectrum corresponding to the modified displacement of the Rayleigh wave. The dominant peak frequency and amplitude in the normalized amplitude spectrum were closely related to the existence of the delaminated defect underneath the test point. The results showed the defects could be assessed more easy for a test performed on the region containing the thinner CA mortar layer. Simple indices were developed to identify the soundness of the CA mortar layer. The indices are obtained based on the difference of the experimental peak amplitude and frequency to the ones calculated from the previously developed formulas for a plate without substrates. As a result, the present technique requires no experimental baseline for the crack assessment.
Ultrasonic Identification of Rail Tracks from Natural Wheel Excitations: Potential for High-Speed and High-Redundancy Rail Inspection
Rail inspection is commonly carried out by ultrasonic techniques that involve a pitch-catch or pulse-echo scheme to detect internal flaws. This method is usually implemented by Rolling Search Units (RSUs) whose maximum test speed is ~ 20-30 mph. These inspections must be carried out by specialized test vehicles requiring careful scheduling to minimize unavoidable disruptions to normal train traffic operations. This paper will present the concept and to-date results from a radically new ultrasonic testing method for rails. This technique exploits the natural wheel excitations of the rail and extracts a stable ultrasonic Transfer Function (Green’s function) of the rail by utilizing pairs of (air-coupled) ultrasonic transducers. Hence the sensing head is completely non-contact and it does not require an active or controlled ultrasonic excitation. The key to this concept is a special signal processing that is applied to the received signals to eliminate or minimize the effect of the (uncontrolled) wheel excitation. Importantly, this method only works if the wheels excite a significant amount of ultrasonic energy at the appropriate frequencies (~ between 20 kHz and 100 kHz). Current studies are aimed at understanding the operational limitations of the method. Results from two field tests conducted at the Transportation Technology Center (TTC) in Pueblo, CO, at testing speeds as high as 80 mph, will be shown to highlight the potential for this “high risk – high rewardâ€ approach. The “high rewardâ€ derives from (a) the possibility to inspect the rail at speeds of regular revenue traffic or higher, which would enable a new concept of “smart trainâ€ that performs inspections seamlessly during regular operations, and (b) the opportunity for great redundancy given the multiple passes of trains over the same track, naturally resulting in an improved probability of detection for any flaw that may be present. While this high-speed mode of testing is not likely to replace conventional ultrasonic inspection, at least at the current stage, it would constitute an important tool in the hands of railroads and regulatory agencies to increase the safety of rail transportation and the efficiency of rail maintenance operations.
Real-time Object Detection Based on R-FCN Network Under Structured Scene of High-speed Railway
In recent years, the object detection technology based on deep learning has made great breakthroughs, greatly improving the detection accuracy.However, most of the existing deep learning detection models are designed for multi-class object detection in natural scenes, which may lead to over-fitting when applied in structured specific railway scenes.Secondly, in order to meet the real-time detection requirements of high-speed comprehensive detection train with a speed of 350 km/h, the detection speed is put forward with extremely high requirements, and the existing deep learning model is difficult to meet the timeliness of high-speed detection.In this paper, we proposes an optimized structured regions fully convolutional Networks(SR-FCN), which change the multiple small objects detection problem into single structured region location problem. The structured prior information of rail track is fused into the various processes of deep learning network including that sample construction, proposal region generation, network building and loss function constraint.By optimizing the regional proposal network as well as the anchor’s traversal number, the locating speed of the railway objects is greatly improved, and the locating error caused by local missing or background interference is avoided, which improves the robustness of detection. The experimental results show that the proposed SR-FCN network can not only achieve a high detection accuracy up to 99.99%, but also maintain a fast detection speed, which can meet the real-time detection at the high speed of 350 km/h.
Damage Assessment of High-Speed EMU Train Axles Via SHM Technology Based on PZT Sensor Networks
As key bearing components, high-speed EMU axles directly affect the operational safety of the vehicle. Timely and accurately detecting cracks, corrosion, deterioration of material properties and other damages that may occur in the axle is the key to ensuring the safe operation of high-speed EMU. Structural health monitoring technology is innovative for diagnosing structural integrity. It has been applied to carbody, bogie frame and other key bearing components of high-speed EMU, but there is no suitable monitoring technique for rotating parts such as axles. In this paper, an axle structural health monitoring technology based on the piezoelectric transducer sensor network was researched. Firstly, the law of transmission of stress wave at the axle surface was studied via a stress wave propagation experiment. Secondly, the structural health status of the axle was monitored in the eccentric loading test. Lastly, a customized sensor network and monitoring system were researched and designed based on the damage locations that may occur at the surface of the axle.
Railway Safety Inspection Intervals Based on Damage Tolerance and Fracture Control Theory
The concept of damage tolerance (damage tolerance methods) has been widely used in the inspection intervals of aircraft, however, for existing railway vehicle structural parts, the establishment of inspect intervals is determined heretofore empirically. Safety inspections are designed (performed) to detect cracks potentially existing at known locations. In this paper an example of bolster, is presented demonstrating how the damage-tolerance theory is implemented (adapted) to establish inspection intervals. Furthermore, it is very important to realize the health management of the structural parts of the railway vehicles and thus the life-cycle prognosis of the train based on the damage-tolerance method.
A Support Vector Regression-based Approach Towards Decentralized Fault Diagnosis in Wireless Structural Health Monitoring Systems
The reliability of sensors in structural health monitoring (SHM) systems is affected by sensor faults that compromise the quality of monitoring, causing erroneous judgment of structural conditions. To ensure reliable operation of SHM systems, techniques for diagnosing sensor faults have been proposed. In wireless SHM systems, embedding techniques for fault diagnosis (FD) into sensor nodes is of increasing importance as sensor nodes process data on board and communicate analysis results instead of large sets of raw data. As a consequence of on-board processing, raw data is frequently unavailable and sensor faults remain undetected. In this paper, a decentralized fault diagnosis approach based on support vector regression (FD-SVR) is proposed. Due to the high accuracy of the support vector regression (SVR), which can be achieved even with relatively small data sets, the FD-SVR approach enables wireless sensor nodes autonomously selfdiagnose sensor faults, enhancing the reliability of wireless SHM systems without a need for large data sets to be used for fault diagnosis. The ability of the embedded FD-SVR approach to detect and isolate sensor faults, increasing the reliability of sensors in wireless SHM systems, is validated in laboratory experiments.
Validation of an Ultra-low-cost Wireless Structural Health Monitoring System for Civil Infrastructure
Structural health monitoring (SHM) is critical to ensure safety of civil infrastructure, such as bridges, railways, or pipelines. However, existing SHM techniques suffer from high investment, installation, and maintenance cost. This paper presents an ultra-low-cost and easy-to-use wireless SHM system for civil infrastructure, featuring real-time sensing, on-board data analysis, energy-efficient wireless communication, and Internet of Things (IoT) connectivity. To validate its performance, the proposed SHM system is installed at Itztal Bridge, Germany, for monitoring and analyzing quasi-static horizontal deck displacements in response to thermal loads. In parallel to the ultra-low-cost SHM system, a traditional tethered SHM system, also installed at the bridge, is used in this study for direct comparison and validation. As a result, the ultra-low-cost wireless SHM system is shown to be easy-to-install and sufficiently accurate for monitoring and analyzing the behavior of civil infrastructure under static loads, serving as a low-cost yet high-performance diagnostic tool with IoT connectivity.
Fire and Rescue Operations in Extreme Winds
Firefighters are frequently exposed to extreme weather conditions. However, most fire agencies lack any form of guideline to determine the continuation or suspension of the fire/rescue operations during high speed wind. The purpose of this research is to provide information about the effects of wind speeds and related factors on the operations of firefighters. Wind tunnel tests were conducted on several mock operations that were divided into firefighting, rescue, and first aid. Three measures were used to evaluate the risk of operation. The first recorded the level of danger that firefighters felt as soon as the experiment was over. The second measured the time taken to walk a given path. The third measured number of footsteps deviating from the prescribed path. The test results revealed that the wind speed which caused significant risk ranged from 15m/s to 22m/s. High-risk operations are classified as tasks involved in wearing turnout gears, carrying big light boxes, and walking on slippery surfaces. Medium risk activities involved hand-bound operations, carrying equipment and objects.
A Hybrid Architecture for Building Energy Optimization
Commercial buildings account for 15 percent of total energy consumption in the US alone. Owners and operators of large commercial properties have accordingly installed energy-saving devices and subsystems (such as sensors and controllers) within their building environments. They have also invested in building management software (BMS) to operate as master-controllers for these devices and subsystems. Unfortunately, current BMS implementations make it challenging to sustain optimal performance of these installed energy-saving devices and subsystems. In response, we propose a hybrid model that includes both cloud and on-premise components. This hybrid model: (i) virtualizes BMSs to overcome proprietary interfaces; (ii) offers uniform cloud data models to address inconsistent data representations; and (iii) manages global authentication to expand limited stakeholder access. The model employs an infrastructure “middle layerâ€ (which we call GeoBMS) that connects the “top layerâ€ of building performance applications with the “bottom layerâ€ of existing BMS implementations. By reusing existing on-premise BMS implementations, our hybrid model protects existing investments and accelerates deployments. We offer a simple “proof of conceptâ€ application (which we call EnergyOptimizer) to validate GeoBMS’s capabilities using a museum floorplan as a case example.
Sensing Social Systems: Towards a True Objective Resilience Framework
While infrastructure has historically been the primary focus of resilience assessments, resilient societies are not only a function of physical systems. Urbanization and climate change are unquestionably posing grand challenges that will affect the performance of physical systems. However, they are also imposing new stressors on social systems that may change the ways in which society uses infrastructure. Consequently, true resilience can only be achieved if people and their actions are fully accounted for. This paper presents two new data collection approaches to quantitatively assess human actions in public urban spaces as a preliminary effort to sense and model social systems. Passive infrared sensors integrated into wireless sensing nodes are used to collect anonymous, discrete event data characterizing foot traffic, and security camera video feeds used as continuous data input for computer vision processing. This paper gives an overview of the data collection program and the results from deployments at the Detroit Riverfront. These data sources are used to identify people, quantify their mobility, and classify their activities.
Hierarchical Anomaly Detection Using a Multi-Output Gaussian Process
This paper comprises a description of a data-driven approach to the real-time monitoring of a physical system. Specifically, a hierarchical anomaly detection algorithm that can identify both instantaneous pointwise anomalies and gradual trajectory anomalies is proposed. To detect anomalies, we first construct a multi-output Gaussian process regression (MOGPR) model that can predict, probabilistically, the outputs of the target system. Using the constructed prediction model, we then propose statistical decisionmaking strategies to determine the abnormal operations of the target system by comparing its measured and the predicted responses. The proposed monitoring strategy does both pointwise and trajectory anomaly detection in a single framework. The proposed strategy was applied to detecting abnormal operations of gas regulators. Validating with the actual gas-regulator data demonstrated that it could identify anomalies robustly and accurately.
Linkage Between a 3D Bridge Model and an IoT Information Model
Installation of sensors to bridges is usually drawn on 2D drawings so that human engineers must read them carefully to evaluate sensing data according to the sensor types, locations, directions, characteristics of the member the sensor is attached, installation method, bridge types, defects or deterioration of the bridge, etc. Since this process cannot be automated, it would take a long time and effort to do this work if the number of sensors to be installed to a bridge becomes hundreds or thousands. In fact, due to the progress of Internet of Things (IoT), many wireless sensors could be installed to many bridges. In order to solve this problem, first, a context-rich 3D bridge product model such as Industry Foundation Classes (IFC) should be developed. Then, a sensor (IoT) information model should be developed. Finally, both of the data models should be linked together so that the system can automatically make complicated queries about the relationship between the bridge and sensors, and can infer the bridge behavior based on the sensing data and condition of the bridge. In this research, a bridge product model has been developed by extending the IFC Bridge product data model which is currently being developed and a sensor data model has been developed. Currently, the linkage part between the two data models is being investigated and developed.
Investigation of FBG Sensor Performance in Detection of Delamination Damage in a Half-conical Shape Composite Component
Fibre reinforced polymers (FRP) being used in many critical applications in aerospace, automobile, marine and civil infrastructures due to their superior strength to weight ratio. The increased use of fibre reinforced polymers (FRP) composites for critical infrastructures has created urgent need of structural health monitoring (SHM) systems to ensure smooth and safe operation of the critical infrastructures. Damage detection in FRP structures using embedded FBG sensors is a widely researched area in SHM field. However, maximum utilization of FBG sensors for monitoring delamination cracks in FRP composite components is not fully utilized due to many engineering challenges at application level and also limited understanding of decoding the data obtained from FBG sensors. This paper details an experimental investigation of delamination in a conical shape structure using embedded FBG sensors. In addition to experimentation a detailed finite element analysis was performed. It has been found that FBG sensors data, the reflected spectra of FBG sensors have provided a good assessment of the delamination growth. Moreover, FEA analysis has shown a similar trend to experimental observations.
Maglev Obstacle Detection System Based on Laser Radar
Magnetic levitation train is a new type of rail transit system which uses electromagnetic force to realize traction drive. Monitoring the maglev track and judge whether obstacles intrude into the vehicle safety limitation is very important to train safe operation. Vehicle-borne laser scanning technology has been gradually applied to the deformation detection of highway, tunnel and other scenes. In this paper, we designed a maglev mobile scanning detection system based on multi-sensors. Three laser profile scanners were used to collect track point cloud data, a GPS clock of the NTP protocol and the GPS time, Pulse Per Second (PPS) signal from an inertial measurement unit (IMU) were respectively provided for time synchronization of SICK and Z+F, the IMU and GNSS antenna were combined as positioning and attitude (POS) system. Using the calculated spatial conversion information, the acquisition data of multi-sensors were geo-referenced by coordinate transformation. Using the obtained three-dimensional (3D) continuous point cloud of the maglev track, the original reference data of obstacle-free is indexed by K-D tree as the initial map. For the point cloud to be detected, query point and query distance threshold are set to carry out the range search and feature matching, the point cloud clustering of the obstacle are collected. Finally, the independent obstacles are output by the processing of European clustering. Through the test of maglev data acquisition experiment and obstacle simulation experiment, the accuracy of 3D point cloud is verified, and the detection of obstacles are finished.
Study on Local Scouring Monitoring of Hangzhou Bay Bridge
Hangzhou Bay Bridge is a crossing-sea bridge that spans the Hangzhou Bay of China, which has a total length of 36 kilometers and is the fourth longest crossing-sea bridge in the world. Hangzhou Bay has strong tides and large tidal range, the suspended sediment and bed material have the same particle size, the seabed sediment are complex and changeable, the development of the seabed groove changes rapidly. So local scour of the piles becomes a potential danger for the safe operation of the bridge. The pile of crossing-sea bridge lie in a complex three-dimension flow, and local scour could cause instability of substructure up to whole bridge failures. Facing scour risk, the bridge scour monitoring system is present and set out to deployfor effectively assessment scour risk of piles.. Besides preventing the risks of catastrophic failures, real-time field scour data can also be used to calibrate and refine the existing bridge scour simulation models. This study carried out local scouring monitoring on the sea platform area of the Hangzhou Bay Bridge. Based on the monitoring results, a formula for calculating the local scouring depth of pile under the action of bidirectional flow in Hangzhou Bay was proposed analyzed. At the same time, the evolution of scour holes in offshore platforms are introduced. A real-time bridge scour monitoring system based on fiber Bragg grating (FBG) is proposed based on the study of erosion mechanism. The monitoring system can directly obtain the changes of external pressure, displacement and other parameters. It has the advantages of strong antiinterference ability and high accuracy.
Embedded Fiber-optic Sensing for Low-cost, High Resolution Monitoring in Bridges and Critical Structures
Under a collaboration led and funded by VicTrack, VicRoads, and other Victorian State agencies with funding from the Victoria Public Sector Innovation Fund (PSIF) in Australia, the Palo Alto Research Center (PARC) and University of Melbourne (UoM) are developing Fibridge (overseen by a Project Governance Board chaired by VicTrack), a fiber-optic (FO) smart monitoring system for bridges to enable predictive maintenance using the industrial internet of things (IIoT). The system uses FO sensors attached to bridge structures to accurately measure and estimate parameters indicative of bridge state online, such as structural strain, thermal response, bending moments, shear/impact loads, and corrosion. Fibridge leverages PARC’s low-cost, highresolution, compact wavelength-shift detection technology and intelligent algorithms in combination with UoM and the stakeholder team’s structural engineering know-how to enable effective real-time monitoring, performance management, better reliability, improved safety, and optimized bridge design. While Fibridge is initially targeting road and rail bridges, it will be extendable to other structures with similar maintenance/monitoring pain points. Here we present an overview of the project, enabling technologies, and summarize key results on vehicle load and structural response features detectable, bridge state estimation algorithm development, low-cost optical readout development, and validation test results. An initial proof-of-concept demonstration on a VicRoads highway bridge in Melbourne being monitored with FO sensors has shown promise thus far. At this stage, the technology is being scaled up towards an extended pilot trial on multiple rail, road, and transit bridges in Victoria. A business case for Fibridge to enable cost-effective maintenance is also summarized.
Applying Computer Vision Technology to Health Monitoring for Underground Structure in a Landslide Area
The study employs computer vision technology to explore an innovative instrument for deformation monitoring with an Internet of Things (IoT) system composited of Raspberry Pi, digital cameras and chessboards. Through laboratory and field testing, the instrument was used for settlement measuring for an underground structure in a landslide area in order for more cost-effective methods of slope monitoring. For laboratory testing, the resolution of the instrument was approximately 0.01 cm. For field testing, a set of the instrument was installed in a drainage gallery that was constructed at an 80-m-depth location within the landslide area. The accuracy of field instruments can reach to 0.08 cm when the optimal distance between the camera and the chessboard was 20 m. Moreover, the instrument costs only one tenth prices of traditional monitoring devices such as electrical tilt meters and extensometers. Overall, the instrument based on computer vision technology can be automatically and efficiently employed for settlement monitoring in the landslide with the IoT system.
An Attribute-based Few-shot Learning Framework for Structural Damage Classification
Performances of learning algorithms for structural damage identification in the complicated real-world situations are faced with significant barriers when training a classifier from only a handful of labeled examples. Real structural local damages always contain a variety of different classes in view of material categories, surface roughness situations and structural components where they emerge. However, only limited image collections are formed and annotated with manual labels. Meta-learning can obtain a parameterized model by training with a small labeled training set and its corresponding test set. Inspired by the perceptions of object attributes transferring and few-shot learning, this study proposed an attribute-based few-shot learning approach for structural damage identification with knowledge transfer. The proposed method is expected to achieve good classification performances by few-shot meta learning and avoid underfitting encountered in the conventional supervised learning using only a few training samples. Moreover, the proposed method exploits an attribute-based transfer learning procedure based on prior knowledge extraction from source categories. The proposed framework consists of two loops, which denotes the external few-shot learning paradigm and the internal attribute-based regression NLP model, respectively. Model updating procedures are then established by using samples in support and query sets and iterations of meta-batches
Pixel-wise Crack Identification of Steel Box Girder by Fully Convolutional Network
Steel box girder is the main component of modern long-span cable system bridges. And crack is the primary sign of problems with girders, thus its identification is very important. Lots of works have been done to solve this issue by cutting raw images into small blocks, then segmentation of cracks is treated as classification of blocks. How- ever, this type of method cannot be accurately segment cracks at the pixel level. Based on this deficiency, this paper proposes a new approach for pixel-wise crack identification of steel box girder by fully convolutional network (FCN). FCN is a new kind of network for pixel-wise segmentation, consisting of only convolutional layers, without fully connected layers. This study presents a new FCN, built based on modified U-Net to adapt to crack identification problem. The number of convolutional layers is adjusted to extract more scaled features. Different feature fusion method is proposed to take full advantages of multi-scale convolutional features. Then, a dataset called APESS2018 Steel Girder Crack ID Dataset is employed for training and validating the network. The dataset includes 50 raw images and corresponding pixel-wise label images with a size of 4928 × 3264 pixels, including four classes: cracks, ruler, handwriting and girder surface (i.e., background). Due to the limitation of computation power, the input size of the network is changed as 544 × 544 pixels. Thus, the Crack ID Dataset is necessary to be cropped and regrouped to 2700 sub-images. 94% of these sub-images are train-validating set with 10% validating rate, 6% are testing set. 180 epochs are trained with multi-step learning rate from 0.01 to 0.0001. Finally, the accuracy of proposed method is validated, 98.906% for training accuracy and 97.525% for testing. Different loss functions (Categorical Cross-Entropy loss and Dice Coefficient loss) are discussed to illustrate their influence on results.
A Deep Learning Approach for Impact Diagnosis
Impact on a structure generates an elastic wave that propagates through the structure carrying a wealth of information about the impact event. The paper presents a deep learning (DL) approach for analyzing these wavefields for the purpose of impact diagnosis i.e., identifying the impact location and reconstructing the impact force time-history. Unlike traditional object detection in computer vision, the nature of the impact diagnosis problem requires capturing context from the wavefield evolution i.e., it involves learning across multiple time frames of the wavefield rather than focusing only on a single stationary frame at a given moment. While scanning across multiple time frames provides essential information about the wave propagation phenomenon in terms of its interactions with structural features, boundaries etc., it mandates the use of deep learning models that can analyze this complex wave propagation phenomenon in both spatial and temporal regimes. A unified CNN-RNN network architecture proposed in the paper first reveals the impact location and then utilizes that information to reconstruct the impact force timehistory. The proposed approach is verified using simulated wavefields obtained from the finite element analysis of a five-bay stiffened aluminum panel. Even with moderate network complexity, the proposed model predicts the impact location and impact force time-history with reasonable accuracy. The potential extension of the proposed methodology to an end-to-end vision-based impact monitoring system is also discussed towards the end of the paper.
A DNN-based Civil Structure Semantic Segmentation and Motion Information Identification Algorithm
Camera can capture large amount of visual information of target within its field of view theoretically. This capability allows a camera to act as a sensor for obtaining displacement or distortion of targets at a large range of distance. Considering that there are so many cameras in public society (including the camera in smartphone) and the cameras will record the vibration of structures in an earthquake event, which will be very helpful for structural seismic damage assessment post-earthquake. With the development of AI, people begin to use DNN (deep neural network) to refine more useful information from images and videos. Significant progress in deep learning began in 2012 as computer computing efficiency increased significantly. Since then, deep learning gradually replaced many traditional methods and has become a new direction for many algorithms, including computer vision algorithms. DNN is one of the earliest algorithms to play its role. It has been used widely in the fields of image classification, semantic segmentation, target tracking and so on. In this paper, a DNN is designed to identify the displacement field of a frame structure model subjected to earthquake ground motion on a shaking table. An efficient training sample generation method is also proposed to help train the network. The network is based on traditional network U-Net, which can tell the semantic segmentation information from images. The network consists of an hourglass-type structure with loose sides in the middle. This structure provides sufficient feature extraction capability for the network and allows it to output an image of the same size as the input, which is semantic segmentation information. Another branch of the network focuses on comparing the motional connections between two adjacent frames of video. The displacement field as well as semantic segmentation results of the frame structure model is finally obtained through this network
Temporal Modeling of Point-cloud Evolution for Predictive Structural Assessments
Recent advances have enabled the use of modern remote sensing technologies such as 3D laser scanning and photogrammetry for the long-term health monitoring of structural systems. These new and emerging vision-based technologies have provided an opportunity to create high-resolution 3D point clouds that capture the insitu geometry of structures in a contactless and nondestructive manner, and offer a fundamentally different source of information than conventional sensor systems. However, the complex nature of point cloud data means that there is a need for new methods of analyzing and leveraging these unstructured data sets. Previous work by the authors has shown that it is possible to quantify geometric and colorimetric structural changes in point clouds, such as those caused by plastic deformations or the spread of corrosion, by quantifying spatial and color differentials on a pointwise basis. In this paper, the authors extend these efforts to longer-term life-cycle modeling by characterizing these differentials as stochastic processes. Point cloud differentials, both geometric and colorimetric, are parameterized via convex hull computations and then fit to vector autoregressive time-series models. These parameterized models can then be used to forecast future condition states of a structure. The results of experimental validation of this process on both synthetic and laboratory specimens is presented as well. The presented experiments illustrate the robustness of the autoregressive approach to varying process models, as well as to measurement noise. The flexibility of the approach with respect to differences in structural materials and flaw topologies is studied as well. The results indicate that the accuracy of this overall approach to point cloud analytics is dependent on the quality of the generated point cloud data, as expected. However, it is also dependent on an understanding of stochastic life-cycle dynamics, most notably if the underlying stochastic process strongly deviates from gaussian assumptions of normality, and highlights avenues for future work.
Monitoring Post-tensioned Miter Gate Diagonals Using Vision- based Vibration Measurements
Miter gates are structures that act as both the damming surface and doorway of lock chambers and are commonly found on U.S. rivers. The geometry of a miter gate is such that the structure is similar to a very deep, cantilevered beam with a channel-like cross-section. Generally, a miter gate is assumed to have very little torsional stiffness, which is an issue due to the torsional loads imparted on the gate as it swings open and closed through the river. Moreover, the channel-like cross-section of the gates means that the gate will torsionally deflect due to its own weight. Thus, long, slender, posttensioned steel members (termed diagonals) are attached across the diagonal dimensions of the miter gate to address the lack of torsional stiffness and counteract the twist of the gate. Maintaining an appropriate level of tension in these diagonals is of critical importance to both the structural performance and serviceability of the miter gate; however, the number of diagonals that need to be monitored, the difficulty of accessing the diagonals, and the geographic distribution of miter gates makes traditional contact sensors economically impractical for monitoring the tension in diagonals. Accordingly, this study investigates non-contact methods for monitoring the tension in miter gate diagonals. The expected low frequency of vibration of the diagonals makes vision-based methods attractive. Initial efforts of this study are focused on the feasibility of an approach wherein a region of interest of the frames of a video of a vibrating diagonal is used as a virtual sensor from which the displacement of the diagonal can be estimated by means of optical flow measurements. Using the time history of displacement obtained from the virtual sensor, the dominant frequencies of vibration of the diagonal are estimated and used to determine the tension in the diagonal using beam theory. The efficacy of the approach is demonstrated by means of a scale-model lab experiment.
Dense 3D Displacement and Strain Measurement Framework of Miter Gates Using Computer Vision
This paper investigates the framework of vision-based dense 3D displacement and strain measurement using a digital camera. The framework has three components: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected accuracy of the measurement, and (iii) selection of the measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. Physics-based graphics model (PBGM) is used effectively to simulate the vision-based measurements in a photo-realistic environment and evaluate the performance of different methods and measurement settings by comparing the estimated values with the ground-truth values. The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.
Deep Learning-based Damage Detection of Miter Gates Using Synthetic Imagery from Computer Graphics
Structural inspections of large, difficult-to-access infrastructure like dams and bridges are often time-consuming, laborious and unsafe. In the United States, federal and state agencies responsible for managing such infrastructure assets are investigating the use of unmanned aerial vehicles (UAV) to allow for remote data acquisition. Processing the large amounts of data acquired by the UAV remains a challenging task. Over the past four years, researchers have been investigating deep learning methods for automated damage detection through image classification and more recently, the use of semantic segmentation where each pixel in the image is given a certain label. For such algorithms to work effectively, deep neural networks need to be trained on large datasets of labelled images. The generation of these labels for semantic segmentation is a very tedious process as it requires each pixel in the image to be labelled. This paper investigates the use of computer graphics to automatically generate synthetic imagery for the purposes of training deep learning algorithms for vision-based damage detection using semantic segmentation. The significant advantage of this is the automatic generation of precise semantic labels due to the implicit information in the developed graphics models. Parametric noise-based graphics texture models are created for defects such as cracks and corrosion and for other features such as vegetation growth, and dirt. The parameterization of the texture models allows for generation of a range of different surface conditions, thereby providing increased flexibility over data generation. To demonstrate the benefits of the proposed methodology for synthetic data generation a virtual environment of inland navigation infrastructure including miter gates and tainter gate dams is created. The developed texture models are applied to the virtual environment to produce a photo-realistic model. Synthetic image data is then rendered from the developed model and used to demonstrate the efficacy for training deep learning-based semantic segmentation algorithms for damage detection.1
A Compressed Sensing Approach to Operational Modal Analysis Using Phase-Based Video Motion Magnification
Phase-based video modal analysis is a modern method of extracting a modal model from a video of a structure’s vibration response. The high spatial resolution inherent to cameras results in high-resolution mode shapes, an obvious advantage in any damage detection application. Unfortunately, the method is limited by the sampling rate of the camera, meaning the high-frequency modes often vital for damage localisation are unobtainable. This paper presents a novel application of compressive sensing to the phase-based video modal analysis method. Compressive sensing allows for signals to be recovered from far fewer samples than traditionally required by the Shannon-Nyquist theorem, thus extending the measurement range of a standard camera.
Computer Vision based In-plane Strain Field Monitoring for Identification of Local Damages in Structural Members
For monitoring local damages like thickness reduction due to corrosion and failure at bearings in steel plate-girder bridge, the in-plane strain field variation measurement can be utilized. It is known that, conducting the strain measurement in actual bridges especially near the ends of girders and bearings are difficult. In this research, computer vision based two dimensional digital image correlation (2D-DIC) technique is applied to obtain the strain field data for monitoring local damages. In order to validate the camera employed for computer vision, tensile test experiments are conducted on aluminum plates, with and without hole, for multipoint strain data acquisition and results are verified using conventional strain gauges and 3D-DIC results. Next, bending test experiments are carried out for a typical aluminum plate-girder bridge model specimen at laboratory level, under different loading conditions. Camera vision based monitoring is utilized for estimating the in-plane strain field variation near the ends of girder and the results are validated using the strain gauge measurements. As the applied load is minimal, the captured strain values are of small order, however, the camera based 2DDIC results are in good agreement with the strain gauge data.
A Topology-Aware 3D Reconstruction Algorithm for Long-Span Cable-Stayed Bridges
3-Dimensional reconstruction (3D reconstruction) generates a 3D computer model of a real object or scene from data such as images, it involves many stages and open problems. Existing methods focus on point clouds and reconstructed polygonal mesh within Manhattan-world constrains in urban scenes reconstruction. However, when dealing with structures like steel truss cable-stayed bridges with complex topology (i.e., connectivity and genus), existing methods fail to recover an appealing polygonal mesh from highly unstructured and noisy point clouds. A topology-aware 3D reconstruction method which can obtain high-level structures and low-level shapes is proposed in this paper. A convolutional neural network and point cloud network is designed to encode multi-view images and 3D point cloud into a compact code, which is then decoded into structure layouts (i.e., a hierarchical binary structural parsing tree) and 3D shapes (i.e., leaf nodes on the binary tree) by designing a recursive neural network and a distance field network respectively. These high-level structures and low-level shapes constitute a 3D digital model.
System Identification of Large-Scale Bridge Model using Digital Image Correlation from Monochrome and Color Cameras
Infrastructure systems and key components such as bridges are usually considered for structural health monitoring especially with the increasing deterioration and rising need for repair or replacements. Moreover, in cases of extreme loading, e.g. earthquake, the dynamic response of bridges and their condition after the event need to be rapidly evaluated. This is to make decisions with respect to opening the bridge for traffic, for instance, if no immediate repair is needed. Using modern technologies and non-contact methods such as wireless sensing or vision- based tracking, e.g. target-tracking Digital Image Correlation (DIC), can provide to the means to monitor our infrastructure for cases of extreme events and advance the current state-of-practice in SHM. Vision-based monitoring systems still face several challenges such as the high cost of high-speed or monochrome cameras used for monitoring. Therefore, it is beneficial to assess the possibility of using commercial or lower-end cameras for SHM purpose. In this study, an application of DIC for monitoring the behavior and seismic response of a large-scale bridge model behavior was applied using commercial DSLR cameras and high-speed monochrome cameras. The bridge model was a two-span concrete bridge that was tested on multiple shake tables under white noise and incremental seismic excitations at the University of Nevada, Reno. The objectives of the tests were to: (1) compare the monitoring performance between commercial and highend monochrome cameras; (2) determine the modal properties of the bridge using system identification from DIC measurements and then compare it to the results from conventional contact sensors, e.g. accelerometers. The results show that feasible hardware such as commercial DSLR cameras can provide reasonable means to monitor and capture modal properties of bridges.
Damage Visualization of Scattered Ultrasonic Wavefield via Integrated Highspeed Camera System
In this study, a damage was successfully visualized through scattered guided acoustic wavefield reconstructed via an integrated high-speed camera scanning system. By employing a sample interleaving and an image stitching technique, the data requisition rate for the high-speed camera was enhanced nearly 250 times in order to overcome the current limited performance due to inadequate on both spatial resolution and temporal resolution, i.e., sampling frequency, for guided wavefield measurement. Further, many experimental parameters, e.g., specimen design, field of view, speckle size and signal frequency, as well as the camera system were carefully designed, integrated and optimized to enable capture of propagating guided waves excited by a piezo actuator on a surface of the structure. As such the in-plane wave displacements could be maximized and extracted using digital image correlation (DIC). A very compliant HDPE plate with an added mass for mimicking damage was chosen for the proof of concept. The first fundamental SH and S0 guided wave modes under continuous wave excitation frequency of 14 kHz were successfully detected by the camera and extracted by DIC, and the disturbance at the damage region was clearly observed in the reconstructed scattered wavefield. A damage imaging condition named wavenumber index (WI) that was previously developed and applied to visualizing the hidden delamination in composites was further adapted and improved for the wavefield reconstructed via high-speed DIC.
Infrastructure Mapping and Inspection using Mobile Ground Robotics
This paper describes two autonomous robotic platform and the attendant decision support tools needed to identify, quantify, and localize defects on concrete surfaces. The platforms presented consist of mobile ground robots equipped with visual sensors (cameras, lidars) that collect the data required to perform simultaneous localization and mapping (SLAM), and to detect defects in images. The location and size of defects is calculated by projecting image defect masks onto the 3D map created using SLAM. Compared to existing sensor platforms, such as terrestrial laser scanners, data collection using the proposed system is extremely rapid. Defect detection and quantification is fully automated using state-of-the-art deep learning, image processing, and point cloud manipulation algorithms. Results employing this platform for surface defect characterization at a concrete structure are presented to illustrate its capabilities.
VR Based Strabismus Diagnosis using Image Processing
Strabismus is an eye movement disorder that the eyes do not properly align with each other when looking at an object. Currently, the ophthalmic prism with two nonparallel planes is used to diagnose the strabismus angle. When the proper prism is placed over the misaligned eye, there is no movement of the eyes when the cover is switched back and forth between the eyes. The strabismus angle is then identified after checking the parameter of the prism. However, the whole process is time-consuming and the results depend on the doctors’ experience. In this study, an automated strabismus diagnosis technique using a VR device is developed. A VR display system is built to simulate the normal strabismus diagnosis steps, in which screens are changed alternately between on and off stages. Patients watch specially-designed 3D scenes, and the eye motions are recorded by two IR cameras. An image-processing based pupil tracking technique is then applied to track the eye movement. After recording the eye position, two strategies for strabismus angle estimation, stepwise approximation and direct measurement, are implemented. The stepwise approximation measures the angle with the feedback calibration process, while the direct measurement converts the eye displacement to the strabismus angle after considering the eyeball diameter. With the proposed process, the strabismus angle is accurately identified. To demonstrate the performance of these techniques, experiments are carried out on various persons, including strabismus patients. The results are compared to the results of doctor’s diagnosis, which clearly indicates that these techniques could identify the strabismus angle with high accuracy and efficiency.
Pop-outs Segmentation for Concrete Prognosis Indices using UAV Monitoring and Dense Dilated Convolutions
Efficient inspection and accurate prognosis are important issues for aging concrete structures. In particular, volcanic aggregates that contain moisture can freeze and expand, resulting in "pop-outs". This pitting corrosion is sparsely distributed; and hence, frequently occur in small regions on the surface of concrete structures. For this reason, it is difficult to accurately count pop-outs and measure their size. If we can automate the prediction of the pop-out region, then repair targets can be set for concrete prognosis. In this paper, prototype concrete segmentation tool for application to sparselydistributed pop-outs was developed to extract damage features from drone images. We propose dilated convolutional networks with several four- and eight-branch atrous sampling rates. Specifically, we applied the methods to 40 drone-monitored images with size of 6,000×4,000, partitioned into 16,000 unit images. We computed pop-out prognosis indices to support repair actions that make sense for dam managers.
Vision-based Decision Support for Flood Risk Assessment Using Google Street View Images
Flood is one of the most common and destructive natural disaster worldwide, both in terms of economic losses and human casualty. With the expected sea level rise for the next century, assessing flood risk become critical for coastal area residents and governments to make effective decisions about risk mitigation. To this end, Louisiana state in the U.S. have launched Comprehensive Master Plan and laid out $50 billion USD for coastal protection and restoration. However, most data for assessing flood risk in Louisiana are obsolete that were collected from street-level surveys in 1991 since it is too expensive to collect and update comprehensive data. As a result, this study proposes a vision-based approach using deep learning that can collect comprehensive data effectively and efficiently without human-involved street surveys. The proposed approach analyzes Google street view (GSV) images and simultaneously predicts several attributes of buildings that are necessary for assessing flood risks. The proposed approach can predict foundation type, height, and building type. Then, individual home or business owners can use a tool that visualize the flood risks to decide what measures to take to protect their assets. Also, local planners and policy makers will be able to develop “adaptation timelinesâ€ that prioritize infrastructure projects using the assessments of current and future flood risks.
Computer-Vision Based UAV Inspection for Steel Bridge Connections
Corrosion on steel bridge members is one of the most important bridge deficiencies that must be carefully monitored by inspectors. Human visual inspection is typically conducted first, and additional measures such as tapping bolts and measuring section losses can be used to assess the level of corrosion. This process becomes a challenge when some of the connections are placed in a location where inspectors have to climb up or down the steel members. To assist this inspection process, we developed a computervision based Unmanned Aerial Vehicle (UAV) system for monitoring the health of critical steel bridge connections (bolts, rivets, and pins). We used a UAV to collect images from a steel truss bridge. Then we fed the collected datasets into an instance level segmentation model using a region-based convolutional neural network to train characteristics of corrosion shown at steel connections with sets of labeled image data. The segmentation model identified locations of the connections in images and efficiently detected the members with corrosion on them. We evaluated the model based on how precisely it can detect rivets, bolts, pins, and corrosion damage on these members. The results showed robustness and practicality of our system which can also provide useful health information to bridge owners for future maintenance. These collected image data can be used to quantitatively track temporal changes and to monitor progression of damage in aging steel structures. Furthermore, the system can also assist inspectors in making decisions for further detailed inspections.
Optimized and Tailorable Speckle Pattern Generation Approach for Digital Image Correlation Applications in SHM
A novel speckle pattern development technique is introduced to create a parametric space to allow for implementation of the Digital Image Correlation (DIC) method in Structural Health Monitoring (SHM) applications. DIC is a non-contact, vision-based deformation measurement method that has been widely used in mechanical testing and material characterization, as well as in structural testing applications. Being scale invariable, this technique has been used to measure full field 3D deformation as well as to identify damage occurring on a large range of scales from transportation bridges all the way down to material microstructure measurements. DIC tracks subsections of images taken during the deformation process to provide full field displacements and maps. To accomplish this goal, surface patterns are used when employing stereoscopic vision and the results with respect to deformation resolution vary due to both hardware and software reasons. In general practice, spraying and airbrushing are used for speckling in a scale from millimeters to meters, while stencils and specialized markers are applied to track deformation on bigger structures, the quality of which is dependent on the user’s experience. In this paper an optimized pattern development technique is proposed to numerically and on-demand generate speckle sizing and distribution-controlled speckle patterns to reduce the uncertainty and increase the usefulness of DIC measurements at variant scales. The quality of the pattern is achieved by extracting features from a biotemplating inspired pattern and merging them with size control parameters for optimized pattern generation for given FOV sizes. Features that improve the quality of the speckle pattern are subsequently extracted via a deconvolution within the frequency spectrum. Gradient information controlling properties of the high-quality pattern are then synthesized and convolved with size and distribution controlling properties of a numerically generated pattern to produce optimized patterns which can then be transferred onto the specimens/structures. An error analysis study shows that the optimized pattern provides better results as compared to traditional speckling methods and that of the original bio-templated image. While the systematic bias in the error remained comparable to that of standard speckling methods, the random bias error reflects an 80% reduction across the entire field of view investigated. The viability of producing multiscale deformation information using this novel approach is assessed by a number of numerical tests.
Pruning Deep Neural Networks for Efficient Edge Computing in Internet of Things: A Structural Health Monitoring Case Study
Condition assessment of civil infrastructures is a key application of Internet of Things (IoT) while edge computing is a crucial component of IoT. In this context, swarms of autonomous inspection robots (e.g., unmanned aerial vehicles (UAVs)), that can replace current labor-intensive manual inspections, are examples of edge devices. Such devices need to have on-board data analysis capabilities for efficient inspection path planning. The incorporation of deep learning algorithms into these edge devices for damage detection is a challenging problem since these devices are typically limited in computing and memory resources. For instance, deep transfer learning, which leverages the advantages of pre-trained models, is usually used to detect a new type of damage when there is no sufficient training data. However, the pre-trained models are typically large in size and therefore are not resource efficient for those edge devices. This study introduces a solution to achieve quick inference and low memory demands through transfer learning and network pruning. Network pruning, inspired by the biological brain behavior, eliminates the redundant neurons of the pre-trained network with an iterative pruning and fine-tuning process. Results from comprehensive experiments on two pre-trained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in details with respect to damage detection performance, memory demands, and the inference time for damage detection. The experiments show that with the NVIDIA Jetson TX2 GPU, simulating the on-board computing platform, the proposed approach achieves an inference time which is nine and four times faster than the original VGG16 and ResNet18 networks, respectively. Also, the network size is reduced by 80% and 95% for the VGG16 and ResNet18 net- works, respectively. Results demonstrate that the proposed approach significantly enhances resource efficiency for field applications without decreasing damage detection performance.
Modal Imaging of Portsmouth, New Hampshire Bridge for Video- Based Structural Health Monitoring
We compare the results of modal imaging [1], a method for measuring operational deflection mode shapes and frequencies in video, to the predictions of a finite element simulation, as well as accelerometer measurements, of a Portsmouth, New Hampshire bridge. The finite element simulation is based on schematics obtained from the New Hampshire Department of Transportation. We also compare the results of modal imaging on the bridge to previous visual analysis from [2], and discuss some of the algorithmic differences that appear to improve results.
UAV-Based Non-Contact Fatigue Crack Monitoring of Steel Structures
A novel computer vision methodology is proposed to monitor fatigue cracks of steel structures using unmanned aerial vehicles (UAV). Through a short video recorded by a consumer-grade camera carried by a UAV, this methodology tracks the surface motion of the structure under fatigue loading and detects fatigue cracks by revealing the differential motion patterns induced by the cracks. Three main steps are developed to process the video for fatigue detection. First, the global motion of the UAV embedded in image frames in the video is compensated through geometric transformation. The transformation matrices are computed and applied to the video frames to obtain motion-compensated frames. Next, feature points are identified from each image frame as natural targets to track the surface motion. Subsequently, with the compensated surface motion tracked by the feature points, a localized circular region scans the entire surface to reveal differential movement patterns for crack identification. Compared with traditional computer vision techniques that are based on edge features for crack detection, the proposed method detects the breathing behavior of fatigue cracks; therefore, it is able to distinguish true cracks from crack-like features and structural boundaries. Laboratory validation is performed to validate the proposed methodology.
Hierarchical Fisher-information-matrix-based Clustering
In this paper, a clustering approach, namely Hierarchical Fisher-information-based Clustering (HFC), is proposed for clustering the similar elements in a structure, based on their effect on the modal parameters and dynamic behaviour of the structure. This clustering approach is indispensable and valuable for any damage localization approach, as usually in practice, the SHM systems include very low number of sensors compared to the number of elements of a structure (or degrees of freedom). Therefore, a clustering approach is a great tool in assessing the possible localization resolution. Furthermore, a clustering approach is necessary to be used for one of the powerful tests, i.e. MinMax test, in Statistical Subspace Damage Localization (SSDL) method. The robustness of the SSDL method on localizing damage in real structures was demonstrated in the literature. In here, the HFC clustering approach along with its effects on the damage localization results for a real test structure, the Yellow Frame, will be presented. It will be shown that the HFC approach can robustly cluster the similar elements of a structure compared to a well-known clustering method, i.e. k-means. The results will be shown and compared.
Reinforcement-learning-based Identification of the System for the Purpose of Structural Change Detection
Reinforcement learning provides an edge in many problems of mechanical engineering. That is because its ability to solve problems on-the-fly based on interactions with initially unknown environment. In this paper the reinforcement learning approach to identification, adaptive control and structural change detection is presented. The approach consists of training feedforward artificial neural networks in a task of adaptive control of an unknown and timevariable system. The nets are used to build system model and propose optimal control prescription to actively suppress vibration of a selected system element. The difference between expected and obtained quality of outcome is used as a damage indicator - in a novelty-detection-based framework. A series of numerical experiments for a 5DOF system is used to evaluate method’s performance. It is shown that the method is able to operate for both impulse and white noise external excitation and can work despite random and significant changes of system parameters.
Visual Analysis for PCA-based Skin-stringer Debonding of Composite Stiffened Panels
The debonding between the skin and the stringer flange in composite aircraft wing panels is a critical aspect in terms of the resulting load-carrying capability of the full panel, and selection of reliable NDI inspection techniques. Usually, the run-out of the stringer is the point of the onset of the debonding under the in-service loads. The detection of the debonding onset and the measurement of the debonding length stringer/skin, has been object of this work by applying a SHM system. It consists of Fiber-Optic sensors in the frequency range 1 Hz to 250 Hz. In this work, the Principal Component Analysis (PCA) technique has been combined with Visual Analytics methods to identify and localize possible damage over aeronautical composite structures, so that skin-stringer debonding detection is made easier and friendly through visual exploration of real structure images. The PCA-based algorithm, looking for significant deviations between a baseline set of strain measurements and the current structural response, has been improved for more efficient damage identification. The “normal behaviorâ€ strain range, based on projections on the principal components, has been automatically tuned to the specific distribution of deviations themselves. In this way, an increased number of possible damage is detected. In addition, to exploit at best the obtained results, a Visual Analytics application has been designed and implemented, letting domain experts to navigate an image of the structural component under test, collecting information about: occurrence of possible damage, location of possible damage, strain measurement index position-based (maximum, minimum, mean), strain deviation between healthy and current states; all in an integrated way. The proposed combination of PCA and Visual Analytics has been applied and verified on a small stiffened panel. The debonding between the skin and the stringers has been provoked by a low energy impact, induced in the center of the skin, between the two stringers, with an energy level of 15 J.
Applying the Concept of Complexity to Structural Health Monitoring
Structural Health Monitoring (SHM) is the process of implementing an online and automated damage detection strategy for aerospace, civil and mechanical engineering infrastructure. Since 2000, the authors have addressed SHM as essentially a problem in statistical pattern recognition, and they continue to believe that this framework is the most appropriate way to develop SHM solutions. The detailed approach is based on a four-step paradigm that includes: (1) Operational evaluation, (2) Data acquisition, (3) Feature selection/extraction, and (4) Statistical model development for feature classification. A current shortcoming of this paradigm is that almost all feature selection is done in an ad hoc manner and is often based solely on engineering judgement and experience of the people developing the SHM system. Furthermore, very little effort has been made to demonstrate that these features are consistent with experimental observations of material damage evolution or the current physical models of damage initiation and evolution. Partly to address this shortcoming, the authors reintroduce previously-proposed ‘fundamental axioms’ of SHM to show how solutions might be organized and informed in their light. In fact, the discussion is focused on the most recently-added ‘axiom’: damage increases the complexity of a system, and what is meant by the term complexity. Often entropy measures - either thermodynamic or information entropy, are used to quantify complexity. However, there are many measures of entropy extant in the literature. This paper will show the application of several measures of information entropy to a numerical example of a system with increasing levels of damage present. A simple proof that damage leads to an increase in entropy is presented.
Deep Learning-based Data Anomaly Detection in Rail Track Inspection
Comprehensive inspection trains (CITs) run over the whole railway network in China and collect a huge amount of data. Occasionally, due to equipment faults or environmental interference, data anomalies appeared which affected the subsequent data processing and analysis. Visual inspection of these anomalies is time-consuming, tedious and subjective. A more automatic way is desired. In this study, this task is considered as an image classification problem to leverage the power of deep learning, which has become a powerful tool in almost every research area in recent years. The multichannel data collected from CITs are normalized, windowed and encoded into images to build a training data set. Each sample is marked with a label that represents the condition in which it was collected. A convolutional neural network (CNN) is then formulated, in which the filters are set to only slide the horizontal direction to imitate humans’ judgment process with the assumption that translation invariance only exists in this direction. Different from the conventional approaches that can only detect data anomalies of a single channel, this deep learning-based approach enables to further integrate multichannel information to find out the cause of data anomalies. It is shown that the approach can achieve satisfactory accuracy in either binary and multi-class classification and thus release the practitioners from the tedious task.
A Temperature-driven One-Class Support Vector Machine Method for Anomaly Detection
Competent data-driven anomaly detection methods should catch the meaningful changes in measurements due to the structural abnormity. However, the distinct thermal effect may produce significant temperature-related fluctuations in the strain measurements when compared with the reflections due to the real structural damages. This paper presents a temperature-driven one-class support vector machine method, designated as Td-OCSVM, for anomaly detection, which introduces the idea of blind source separation (BSS) for thermal feature extraction and further employs OCSVM for anomaly detection. First of all, the temperature-related strain variations can be investigated and revealed by employing independent component analysis based on the criteria of maximization nongaussianity, which is one of the most popular solutions for BSS problem. Afterwards, the OCSVM is adopted for anomaly detection on those separated temperature-induced responses. In case study, the Ricciolo curved viaduct in Switzerland is utilized for the purpose of evaluating the proposed method. This viaduct had been monitored in both construction and in-service period. The data obtaied during the construction period is labelled as anomalous condition, which is expected to be identified by Td-OCSVM. The performance of Td-OCSVM is also compared with OCSVM only with temperature-driven process. The interpretation results can demonstrate the outperformance of Td-OCSVM with the higher detectable ability when compared with OCSVM.
Damage Classification Using Labelled and Unlabelled Measurements
The use of a Gaussian mixture model for probabilistic and semi-supervised learning is introduced here for damage-classification. Semi-supervised learning allows for pattern recognition algorithms to learn from the available labelled and unlabelled data; this is particularly relevant to SHM, as labelling measurements from engineering systems is often impracticable and expensive. In this work, semi-supervised learning is shown to improve the classification performance for simulated SHM data by utilising the available unlabelled measurements alongside a set of labelled data.
Damage Localization Using Pattern Recognition Techniques and Statistical Dissimilarity Analysis
The increasing expansion of standards and regulations aimed to guarantee the safe operation of different types of structures and materials ---which are extensively used in buildings, airplanes, transformers, machinery, etc.--- has driven the development and implementation of systems intended to supervise the state of the structure so critical failures may be predicted and avoided. Nevertheless many of those systems are expensive and require the structure to be inactive in order to evaluate its state, so it is frequently necessary to use external equipment and specialized technician teams, producing therefore a high interest in many sectors of industry, military and academic fields on supporting structural health monitoring (SHM) projects to develop new alternative systems. Among the proposals, the ones that includes integrated sensor networks systems are becoming increasingly popular due to their potential to perform the structure health evaluation even during these equipment are still in use, which results in a significant reduction in the economical and productive impact that other systems cause and the possibility to introduce new evaluation techniques that exploit the sensor and programming flexibility. This paper introduces a SHM system aimed to locate damages on metallic and composite material structures. For this purpose, a methodology is applied in this work that combines statistical analysis of dissimilarity with pattern recognition on the data recollected from the piezoelectric sensors network distributed on the structure surface. This methodology is tested in an aluminum plate instrumented with eight piezoelectric sensors and some masses added to the structure in order to emulate changes in the structure due to a damage. Results shows that it is possible to locate all damages.
Autoencoder Networks for Unsupervised Feature Extraction of Acoustic Monitoring Signals
Manufacturing facilities require autonomous damage detection and monitoring to maintain functionality and reduce maintenance costs, particularly when human intervention is costly or dangerous. In an industrial scenario, damage is typically incurred through the wear or faulting of power transmission systems such as bearings, belts, or motors. The consequences of such damage can be localized, or it can cascade into catastrophic failure across a range of processes and systems, with outsized economic impact. Acoustic monitoring systems can help to prevent such problems through near real-time assessment of process quality and degradations, however robust data analytics play an integral role. In this work, a nondestructive method for identifying and tracking processes and events within a manufacturing facility is presented, based on a high-dimensional analysis of features extracted from acoustic signals after a preliminary segmentation by a convolutional neural network. Most approaches to feature extraction utilize a combination of features manually extracted from time and frequency domain signal representations, often in conjunction with principal component analysis. The process presented here considers the applicability of autoassociative neural networks, or autoencoders, for performing feature extracting in an unsupervised regime. Using experimental data collected from mechanical equipment, features extracted via a conventional approach are compared against features extracted using an autoencoder approach, both for the ability to distinguish between recorded mechanical actuations, and for their ability to track changes in these processes over time. The results of this evaluation indicate that an autoencoder is capable of results comparable to conventional methods. The major limitation of the approach is the need for larger data sets in order to ensure network convergence. Future work includes the incorporation of these autoencoders into a deep learning framework, as well as applications to nonlinear manifold learning.
Combining Edge and Cloud Computing for Monitoring a Fleet of Wind Turbine Drivetrains Using Combined Machine Learning Signal Processing Approaches
Today, we are at the beginning of Industry 4.0. Machines are increasingly sensorized and equipped with internet connection. Especially the emergence of 5G is a game changer in this regard. It becomes possible to send data at high speeds to cloud computing data-centers. However, streaming all data is deemed to be unfeasible. It is more advantageous to use the additionally available bandwidth to drastically increase the number of connected sensors. Thus, on-board processing of the data directly at the edge is necessary. Preprocessed data is then sent to a cloud data-center for processed using computationally intensive algorithms. An application that can leverage this new context is condition monitoring (CM). By balancing the edge-processing with central processing the detection potential and insights in system behavior can increase. Advanced features are extracted at the edge. Long-term trending, anomaly detection and learning approaches are performed centrally together with more computationally intensive algorithms. This paper illustrates an integrated monitoring approach for wind turbines exploiting this Industry 4.0 context. Our combined edge-cloud processing approach is documented. We show edge processing of vibration data captured on a wind turbine gearbox to extract diagnostic features. Focus is on statistical indicators. Reallife signals collected on an offshore turbine are used to illustrate the concept of local processing. The NVIDIA Jetson platform serves as edge computation medium. Furthermore, we show an integrated failure detection and fault severity assessment at the cloud level. Health assessment and fault localization combines state-of-the-art vibration signal processing on high frequency data (10kHz and higher) with machine learning models to allow anomaly detection for each processing pipeline. Again this is illustrated using data from an offshore wind farm. Additionally, the fact that data of similar wind turbines in the farm is collected allows for exploiting system similarity over the fleet. Again this is illustrated using data from an offshore wind farm.
Concrete Micro Crack Detection and Segmentation Using Adaptive U-Net
The identification of concrete micro cracks has always been a difficult problem in crack detection, because the small cracks provide less effective detail information in the image, and the distribution is in disordered condition and irregular, complex environmental background. The existing semantic segmentation network based on deep learning can extract the crack location information to the pixel level, but micro cracks are too small that the proportion of the target pixel is very low, resulting in data imbalance which is the mainly reason that the small crack target is often ignored as the background target and can’t get the fine details. The accuracy of fracture identification is seriously affected. Pointing to this problem, this paper presents a small crack detection and segmentation method based on adaptive U-Net network. Aiming at the problem of uneven distribution of small crack sample data, firstly this method uses the oversampling strategy which expands the number of tiny cracks in the sample; at last due to the unbalanced characteristics of tiny cracks, the appropriate loss function is selected to adapt the U-Net which makes the model pay more attention to the tiny target. Experimental results show that the proposed method can effectively extract the features of small cracks, improve the similarity of clustering, enhance the semantic segmentation with powerful optimization performance and solve the contradiction between accuracy and speed. This method improves the robustness of small crack segmentation and reduces the risk of matching unsuitably, and can be more accurately detected and segmented than other semantic segmentation models.
On the Application of Domain Adaptation for Aiding Supervised SHM Methods
The lack of available damage state data is a significant challenge within the field of Structural Health Monitoring (SHM). When data are obtainable for a given system of interest, a variety of machine learning approaches have been successful in addressing a range of supervised SHM problems. However, these methods assume that the training and testing data sets are drawn from the same distribution; as a consequence damage state data must be collected for each new structure and/or damage scenario considered, which is often infeasible and/or not economically viable. In these contexts it is useful to transfer knowledge obtained from known damage state data to different, but related contexts (or domains) of interest. By utilising transfer learning, knowledge obtained from different structures and/or damage scenarios can be used to improve learners in various target domains. Domain adaptation, a subcategory of transfer learning, is concerned with scenarios where the data distributions across source and target domains are different; and is demonstrated here to be applicable to SHM in a numerical and experimental case study.
Online Damage State Clustering of Cutting Tools Using Dirichlet Process Mixture Models
Tool condition monitoring is an area of research with the main focus on predicting time to tool failure. Ultimately, the goal in the manufacturing industry is to establish a production line with a predictive maintenance strategy in order to reduce waste. One of the most crucial challenges in tool condition monitoring is the availability of descriptive labels of wear states. It is not possible to directly acquire damage labels whilst the tool is in use. Consequently, collecting machining datasets is expensive and time consuming as the process must be interrupted to obtain damage labels. Furthermore, the labels alone cannot be used as an indicator for when a tool may fail. An online semi-supervised learning method is proposed in this paper that allows the use of incomplete label sets and alleviates the need for pre-labelled training data, therefore reducing the costs associated with data collection. The authors apply Dirichlet process mixture models to an acoustic emission dataset, collected during a turning operation. The Dirichlet process mixture model allows data clustering as it is collected without the need to set the number of possible clusters a-priori. This will enable operators to visualise when the characteristics of the cutting process change during machining, avoiding the need for exhaustive measures for tracking tool wear, or the early disposal of tools, depending on the context.
Deep Convolutional Neural Networks for Comprehensive Structural Health Monitoring and Damage Detection
Vision-based structural health monitoring (SHM) has become an important approach to recognize and evaluate structural damage after natural disasters. Deep convolutional neural networks (CNNs) have recently attained a breakthrough in computer vision field, in particular for image classification task. In this article, we adopted deep residual neural network (ResNet) whose residual representations and shortcut connections mechanism has gained significant performance in various computer vision tasks. In addition, we applied transfer learning due to a relatively small number of training images. To test our approach, we used the dataset from the 2018 PEER Hub ImageNet Challenge dis- tributed by Pacific Earthquake Engineering Research Center. This challenge proposed eight structural damage detection tasks: scene classification, damage check, spalling condition, material type, collapse check, component type, damage level and damage type which can be categorized as binary and multi-class (3 or 4 classes) classification problems. Our experiments with eight different tasks showed that reliable classification can be obtained for some tasks. Corresponding above eight tasks, classification accuracy varied from 63.1% to 99.4%. Our approach has attained third place for overall tasks in this challenge. Through the individual observation of training dataset, it is found that there are a large number of confusing images. Therefore, it is believed that the accuracy will be improved after making a precise training data.
SHM Based Damage Detection Using Cointegration and Linear Multivariate Data Analysis: Performance Comparison Based on a Real Case Study
Two alternative methodologies for online data normalization are described and compared: multiple linear regression followed by principal component analysis (MLRPCA) and cointegration (COI). While the former is being used for some time in the scope of SHM, only recently the latter was introduced to the analysis of SHM data. In both cases the statistical classification is performed resorting to the Hotelling T2 statistic. The developed algorithms are applied to a prestressed concrete cable-stayed bridge of which 3½ years of continuous data is available. Three performance indicators are used to compare the two methodologies: one is the number of false positives (incorrectly predicted damage events) and the other two are related to the sensitivity to damage. Several damage scenarios involving small section loss the stay-cables are simulated by corrupting the measured (real) time series with the structural response to the damage events obtained from a finite element model of the bridge. It is shown that both methodologies can provide robust results and reasonable sensitivity to damage.
Learning Condition Indicators for Rolling Element Bearings via Deep Variational Autoencoding
Feature extraction from time-series data is required for condition monitoring of dynamic mechanical systems. However, conventional techniques based on the knowledge of a human expert are not reliable for large and multidimensional datasets. The consumption of grease life in rotorcraft bearings has been studied and grease life models factor in to the determination of airworthiness and maintenance requirements. Features of acoustic emission (AE) signals have demonstrated potentially higher sensitivity to lubrication condition due to increased AE generation through asperity contact, than can be measured in structural vibration signals. The objective of this effort is, therefore, to utilize deep representation learning to automatically extract the most relevant features associated with lubrication condition from time-series data and assess their capacity as condition indicators of bearing grease life. AE and vibration data are collected from an accelerated grease consumption test on an otherwise healthy bearing with reduced grease. The time-series data is transformed into spectrograms and unsupervised learning is performed on these images using a convolutional variational autoencoder. The encodings of these spectrograms in the latent space of the autoencoder and their correlation with experiment time as an estimate of grease consumption were examined, where it was observed in both cases, via dimensionality reduction and by probing the generative model, that though the trends differ, the latent spaces of both AE and vibration data models are highly structured based on experiment time. However, the encodings of AE signals present a clearer and more descriptive trend when compared with vibration signal encodings.
An Automated Non–Parametric Healthy Subspace Method for Unsupervised Robust Vibration–Based Damage Detection Under Uncertainty
An automated healthy subspace method for unsupervised robust vibration–based damage detection under uncertainty is postulated. The key idea lies with the approximation of the “healthy subspaceâ€ as the union of a number of hyper–spheres, with properly determined centers and radii. The method features full automation capability, aiming at eliminating user intervention, and very good detection performance. Its effectiveness is demonstrated via damage detection for a population of composite beams and comparisons with a Multiple Model based and a Random Coefficient Gaussian Mixture model based method.
Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures
The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice.
Unsupervised Damage Detection in Time Varying Environmental Conditions Using an Autoencoder and Support Vector Data Description
Active guided-wave-based structural health monitoring techniques have been widely studied for inspecting large structures. The vast majority of such methods use baseline comparison as a measure to detect damage on the structure. Temperature changes between the times of baseline and test signal acquisition affects wave propagation in the structure, and changes the sensor signals. Such changes, not related to damage, confound damage detection algorithms. Baseline temperature compensation methods are often used to mitigate the effects of temperature but residual compensation errors may still cause difficulties for damage detection and characterization. An alternative is to use a machine learning algorithm and appropriate signal features to discriminate between damaged and undamaged states from sensor signals. Such methods require data acquired on the damaged structure to train the machine. In this paper, we present a novel damage detection algorithm that is only trained on data from an undamaged structure. Results of experimental data analysis demonstrating that the the method of this paper can reliably detect notches greater than 1 cm in length on an aluminum plate under varying environmental conditions with accuracies greater than 99% are presented
Mode Separation of Complicated Guided Wave in Plate-Like Structures Based on Sparse Bayesian Learning Approach
Guided wave based methods has been demonstrated to be the effective techniques for damage detection in plate, tube and pipe etc. structures during the last two decades. However, the nature of guided waves, such as multi-mode, dispersion effect and mode conversion, results in the difficulty of guided wave packets recognition. This work uses a special piezoelectric wafer to generate both Lamb waves and guided shear horizontal (SH) waves in plate-like structures. The latter is employed to detect the damage taking advantage of its non-dispersive properties. However, the SH wave packets without distortion are still disturbed by the other mode wave packets, such as S0 and A0 mode of Lamb waves at low frequencies. In this study, based on a robust sparse Bayesian learning (RSBL) technique, a novel mode separation method is proposed to identify the SH wave packets. An over-complete dictionary is first designed by using the propagating waveforms with various distances. The dictionary is then employed to decompose the received signals containing SH waves and Lamb waves by the RSBL algorithm. The sparseness of wave packet is utilized and the posterior mean vector of the retained basis vectors in the dictionary can be used to determine the propagation distance of wave packet. Furthermore, the posterior uncertainties give a measure of the inference confidence. A numerical study is performed to validate the proposed method and the results show that the proposed method is capable to separate the SH mode wave packets with high accuracy.
Methodology for Real-time State Estimation at Unobserved Locations for Structures Experiencing High-rate Dynamics
Real-time monitoring, condition assessment, and control of structural systems that experience high-rate dynamics are challenging due to these structures operating at timescales below 10ms. Examples of structures that may experience high-rate dynamics include hypersonic vehicles, space crafts, and barriers with active blast mitigation. The realtime monitoring systems used on these structures could greatly benefit from a robust and effective high-rate state estimation methodology that would enable the state of the structure at any unobserved location to be estimated. Any methodology designed for these high-rate systems must account for the challenges associated with data measurement and processing at the considered timescale. This work presents and experimentally validates a methodology for enabling the real-time state estimation at unmonitored locations on a dynamic system that undergoes system level changes (i.e. damage). In this work, the DROPBEAR experimental test bed at the Air Force Research Laboratory is used to validate the proposed methodology for a one-degree-of-freedom system excited with an impact load where a system level change is realized through the dropping of a magnetically attached mass during testing. Results show that the proposed methodology is capable of accurately estimating the levels of acceleration experienced at two unobserved locations in real-time both before and after the mass is detached. A time interval between state estimations of 7.1 ms was achieved during testing.
High Resolution Bridge Mode Shape Identification via Matrix Completion Approach
Mathematical platforms that are able to estimate modal characteristics from mobile sensors are not much investigated. Mobile sensors collect spatially dense data compared to limited spatial density of fixed sensor networks. This feature potentially enables to refine the identified natural mode shapes as well as more robust estimations of other modal characteristics, e.g., natural frequencies and damping ratios. In this paper, highresolution natural mode shape identification of a simple-span bridge using mobile data is investigated. A recent methodology developed by authors is used to reconstruct a full bridge response matrix from mobile data. Matrix completion technique approximates unobserved signals at many virtual stationary locations via a convex optimization procedure. This reconstructed data is then fed in batches into available output-only system identification algorithms to extract modal properties. Mode shape refinement then is performed by superimposing identified results of all considered batches. The accuracy of the matrix completion for signal reconstruction was shown before, however, the performance of the estimated signal for modal identification has not been demonstrated yet. In this study, a numerical case study is examined to compare identification results from this procedure compared to a conventional sensing network consists of fixed sensors.
On the Potential of Dynamic Sub-structuring Methods for Model Updating
While purely data-driven assessment is feasible for the first levels of the Structural Health Monitoring (SHM) process, namely damage detection and arguably damage localization, this does not hold true for more advanced processes. The tasks of damage quantification and eventually residual life prognosis are invariably linked to availability of a representation of the system, which bears physical connotation. In this context, it is often desirable to assimilate data and models, into what is often termed a digital twin of the monitored system. One common take to such an end lies in exploitation of structural mechanics models, relying on use of Finite Element approximations. proper updating of these models, and their incorporation in an inverse problem setting may allow for damage quantification and localization, as well as more advanced tasks, including reliability analysis and fatigue assessment. However, this may only be achieved by means of repetitive analyses of the forward model, which implies considerable computational toll, when the model used is a detailed FE representation. In tackling this issue, reduced order models can be adopted, which retain the parameterisation and link to the parameters regulating the physical properties, albeit greatly reducing the computational burden. In this work a detailed FE model of a wind turbine tower is considered, reduced forms of this model are found using both the Craig Bampton and Dual Craig Bampton methods. These reduced order models are then used and compared in a Transitional Markov Chain Monte Carlo procedure to localise and quantify damage which is introduced to the system.
Identification of Time-varying Structural Parameters by Integrated Wavelet Decomposition and Kalman Filter with Partial Response Measurements
Structural parameter identification has been emerged as an interesting and important problem in structural health monitoring (SHM). Structural parameters directly reflect the dynamic characteristics of structures, so the identification of timevarying structural physical parameters is more meaningful to civil engineering structures. To overcome the limitations of requiring full measurements of structural responses in previous research, an algorithm based on the integration of wavelet multiresolution analysis and Kalman filter with partial measurements of structural acceleration responses is proposed. First, the time-varying structural physical parameters are expanded at multi-scale profile by wavelet multiresolution analysis and a time-varying parametric identification problem can be converted into a timeinvariant one. Then, structural states at all dynamic degrees-of-freedom (DOFs) are estimated recursively by the Kalman filter with partial measurements of structural acceleration responses at previous step. The scale coefficients of the expansion can be obtained by minimizing the error between estimated and observed accelerations. Some numerical examples of identifying time-varying structural parameters of a linear three-story shear type frame are used to illustrate the effectiveness and accuracy of the proposed algorithm.
Operational Vision-Based Modal Identification of Structures: A Novel Framework
Vision-based modal identification of structures is a rapidly-growing field of study in system identification (SID) and structural health monitoring. However, previously proposed methods typically require many complicated computational steps which limit their application for a fast modal analysis. For instance, the majority of available vision-based methods first convert visual data into vibration time signals and then apply common SID techniques. In the present study, a simple and novel approach is introduced to facilitate a complete modal identification of a structure using widely available tools with no need for data conversion from image to signal. The method is based on Proper Orthogonal Decomposition (POD) which originates from the Karhunen-Loeve expansion and decomposes a set of spatial data measured at various instants in time into a minimum number of orthogonal basis functions using the singular value decomposition. To validate the proposed framework, two experimental case studies are investigated: i) a two-story lab-scale shear frame and (ii) a cantilever with continuous mass. We demonstrate that a set of steps proposed here provide an estimate for modal properties with a high accuracy and resolution.
An Improved Subspace Identification of a Nonlinear Soil- Structure System
In this paper, we analyze experimental results from a centrifuge model test simulating seismic response of a pile foundation in soft clay with a cement-treated improvement zone. We apply a robust frequency domain nonlinear subspace identification method known as FNSI to displacement data derived from strain gages along the pile and from a linear potentiometer attached to the superstructure. The earthquake base motion is considered to be a strong excitation capable of introducing nonlinearities in the soil-pile-superstructure system represented by a 2-DOF mechanical model with a coupling spring between the superstructure mass and the foundation mass as well as a ground spring between the foundation mass and the base. Nonlinearities introduced by soil stiffness degradation depending on soil strain level are lumped into the ground spring. Inclusion of a softening cubic spring in the nonlinear model formulation proved to be highly beneficial. The identified modal parameters are compared with those obtained from the time domain linear subspace state space system identification method. Our results demonstrate the power of the FNSI method and show significant improvement in the predictions of the identified nonlinear state-space model despite conditioning issues with the extended input information matrix.
Vibration-based Damage Identification of Sandwich Panels with Truss Core
A damage identification method, based on structural time domain dynamic responses and Teager energy operator, is presented for sandwich panels with truss core in the paper. The dimensionless structural dynamic responses, i.e., dimensionless velocity and displacement, are combined to construct the damage index. Application of the method on sandwich panels with truss core in the cases of single damage and multiple damages with different extents are conducted. Effects of some factors on the proposed method are discussed, including excitation location, excitation frequency, boundary condition, number of points N in Poincare maps. Experiments based on laser Doppler vibrometer are conducted to verify the effectiveness of the method. Numerical and experimental results show that the proposed method is effective in detecting both single damage and multiple damages with different extents. Excitation location plays a very important role in affecting the effectiveness of the method. Excitation frequency has little effect on the method, and there is a great selection space of excitation frequencies. Increasing the boundary condition constraint is beneficial for damage identification. As the number of points N increases, the damage index results also change. There is a reasonable number of points N for the damage identification of sandwich panels with truss core. Besides, Teager energy operator plays a very important role in eliminating the fluctuations caused by boundary condition or environment.
Damage Localization with SP2E Under Changing Conditions
The central concept of vibration-based structural health monitoring (SHM) is the computation of damage sensitive features like natural frequencies or model residues. These parameters are typically observed and evaluated from a statistical point of view following the statistical pattern recognition paradigm. It is of common knowledge that those features are not merely effected by local structural alterations, but also by environmental or operational conditions (EOC). To distinguish or eliminate these, many data normalization strategies exist for linear and even non-linear cases. Besides damage detection under environmental and operational variability, the determination of actual damage positions is desirable for large and remote engineering structures such as onshore and offshore wind turbines or bridges. The recently introduced state projection estimation error (SP2E) method appears to be a promising approach towards damage localization of mechanical systems. This procedure is purely data-driven (output-only) and therefore does not rely on updated numerical physical models (e.g. FE models). It is based on identified, parametric, linear time-invariant (LTI) systems, state estimation and advanced projection techniques. The method significantly differs from conventional modal approaches e.g. the comparison of mode shapes or curvatures. Although laboratory experiments proved a high sensitivity against local structural changes for some systems, damage localization with SP2E under varying conditions has so far not been investigated. This article deals with this important issue. Therefore, simulations of a linear parameter varying (LPV) system were conducted in the healthy and locally altered state. The principal component analysis (PCA) was then applied to distinguish between local and global changes and successfully locate the damage positions. As an important step for validation, these investigations were complemented by a laboratory test. This research helps to improve the understanding of the sensitivity of the SP2E method towards varying environmental and operational conditions. Additionally the applicability of linear data normalization strategies with respect to damage localization was tested. It therefore lays a foundation for future field applications.
Reliability of Probability of Detection (POD) of Fatigue Cracks for Built-in Acousto-Ultrasound Technique as “in-situâ€ NDE
The acousto-ultrasond based SHM technique is quantified as "in-situ NDE" through a probability of detection (POD) method based on a new model assisted approach in the context of fatigue crack detection following the traditional scheduled maintenance concept. A numerical model is developed based on Bayesian method using fatigue test specimen data from multiple PZT sensor paths forming a Unit-cell around the crack hotspot. The model demonstrates that five or more independent specimens’ data can sufficiently provide convergence in results.
Nonparametric Structural Mass and Hysteretic Behavior Simultaneous Identification Using Limited Acceleration Measurement
Identifying damage initiation and development in engineering structures in the form of hysteretic behavior or nonlinear restoring force (NRF) after strong dynamic loading in a nonparametric way is attractive because it is challenging to assume a general parametric model describing the nonlinear behavior of various engineering structures in prior due to their individuality. Although traditional extended Kalman filter (EKF) is efficient in state vector estimation and structural parameter identification using partially available output measurements, a known structural mass is usually required. In this study, a simultaneous structural hysteretic behavior and mass identification approach is developed for multi-degree-of-freedom (MDOF) structures using the EKF with weighted global iteration (EKF-WGI) based on limited available acceleration response. The NRF is modeled with a power series polynomial model (PSPM) as a nonparametric function of unknown structural displacement and velocity responses to be estimated with EKF-WGI. Then, the performance of the approach is numerically illustrated with multi-story structures equipped with two magneto-rheological (MR) dampers using known applied excitations, partially available noise-contaminated acceleration measurements but unknown mass. The effect of different noise levels and structural mass initial estimation errors on both NRF and mass identification results as well as the convergence of the approach are investigated.
Acoustic Emission Monitoring of Hydrogen Induced Cracking in Steel Structures
A single channel Acoustic Emission (AE) system to detect Hydrogen Induced Cracking (HIC) growth in steel structures was developed. The system consists of a commercial AE sensor and an in-house built data acquisition and processing unit. The system is designed for industrial applications, and can be permanently installed on assets operating in sour environments to detect the growth of HIC, and to qualitatively indicate its rate of growth. Previous literature has been able to discriminate HIC-related AE events from other events captured during failure, and has shown that the energy and duration of the AE events can be used for HIC detection. The system developed here was tested in a laboratory where HIC was induced in HIC-susceptible steel coupons. The experimental setup considered here overcame previous limitations, and produced representative test settings that simulate realistic field conditions. Preliminary Ultrasonic Tomography (UT) results, combined with a filtering method from the literature were promising but not conclusive. Additional testing is being conducted to enhance the accuracy and reliability of the measurements.
Rapid Condition Evaluation of Concrete Pavement with Automatic Acoustic Scanning System
The detection of concrete pipe cracks has received great attention in concrete pipe infrastructures. Typical concrete pipe inspection method for the crack detection is visual inspection using closed circuit television (CCTV), and the inspection is performed in an accessible internal cracks on the outer surface (soil side) of a concrete pipe. Therefore, the approach has challenges to detect invisible or internal cracks. The crack evaluation method using mechanical wave propagation in concrete is one of the promising technique to detect and evaluate the concrete cracks. The approach proposed in this paper is to develop a rapid automatic contactless scanning system for detecting the cracks and other damages of fiber reinforced concrete pipe (FRCP) in both inner and outer cracks by using the auto-impacting system and MEMS airborne sensors. The test results show the proposed inspection system allows to detect both internal and surface cracks by calculating wave scattering and energy attenuation. The numerical finite element simulation The authors verified the experimental results using finite element (FE) model. The FE model of guided wave energy represents the intact, inner cracked, and outer cracked condition in concrete pipe. The laboratory testing results demonstrate the potential of the scanning system producing the crack map of the prepared concrete pipe specimen. The crack maps successfully indicate the crack locations and relative damage levels of inner and outer crack. The scanning with movable platform can be applied with the auto-impacting system and MEMS non-contact sensor in the further research.
Static Tactile Sensing for a Robotic Electronic Skin via an Electro-Mechanical Impedance Based Approach
Tactile sensing is paramount for robots operating in human-centered environments to help in understanding the interaction with objects. To enable robots with the sophisticated tactile sensing capability, researchers have developed different kinds of electronic skins for robotic hands and arms to realize the ‘sense of touch’. Recently, Stanford Structures and Composites Laboratory developed a robotic electronic skin which is based on a network of multi-modal micro-sensors. This skin can identify temperature profile and detect arm strikes by embedded sensors. However, one vital aspect of tactile sensing is yet to be investigated: sensing for the static pressure load. Current state-of-the-art tactile sensors mostly are capacitive sensors which can achieve high sensitivity. However, these sensors are liable to damage under high repeating load. In addition, capacitive sensor signals are prone to external noises which will result in complex circuitry for signal conditioning. In this work, an electromechanical-impedance based method is proposed to investigate the response of piezoelectric sensors to the static normal pressure load. The smart skin sample was firstly fabricated by embedding piezoelectric sensor into the soft silicone. Then a series of static pressure tests to the skin were performed. Test results show that this setup can reach a minimal detectable force of 0.5N by using the proposed diagnostic method. Theoretical analysis was then performed to explain the experiment results.
UAV Inspections of Metallic and Composite Aircraft Panels
This work assesses the significance of using a UAV on accelerating and improving the aircraft’s wing panel inspection, both on metallic panels (i.e. Airbus A320 and A380) using a concept of an aerial flow penetrant testing with an on-board UV light on the UAV and composite panels (i.e. Airbus A350) by suggesting an autonomous thermographic imaging approach on a UAV. Although research has been carried-out on automated aerial inspection and maintenance on aircraft parts by using UAVs, no studies have been reported. Most of the work done in this field, implement autonomous ground vehicles or robotic arms for defects detection. Nonetheless, such autonomous mechanisms might not be suitable for all the applications, because ground vehicles might not inspect high facilities, whilst robotic arms might require expensive equipment and appropriate infrastructure. The automated inspection method proposed in this study permits the operator to focus on the results of raw and processed videos (visual and/or IR), without concerning with flight skills, training, and regulations. It is worth mentioning that the operator with a press of the button could start the mission or terminate it by performing an autonomous landing. The implemented condition assessment method is designed to be GPS-independent and perform indoor inspection based-on on-board sensors. Results indicate that the suggested approach could significantly reduce the inspection time, cost, and workload, whilst potentially increase the probability of detection of defects on aircraft wing panels.