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Detection, Localization and Quantification of Anomalies in Mass, Stiffness and Damping Based on Time Series Modelling Using Output-Only Data



In this paper, an improved time series approach for anomaly detection, localization and quantification using output-only data is presented. It was shown in previous studies conducted by the authors that this methodology was able to detect, locate and quantify the changes in the mass, damping and stiffness parameters in a numerical model for noise-free conditions accurately. In this paper, the approach is extended for damage identification when artificial noise is added to the data. This approach is mainly based on the idea that the changes in the structural properties of an infrastructure can be related to the change in free vibration response (displacement, velocity and acceleration) of the structure. In this approach, the sensors of a structure are first categorized into different clusters according to their locations. Then, ARX models (Auto-Regressive models with eXogenous input), where only output data under free vibration are used, are created for different sensor clusters. Building these ARX models for baseline and damaged structures, the location and degree of the anomaly can be identified on the basis of the changes in the coefficients in the models. In order to verify the approach, it is applied to a numerical structural model representing a laboratory specimen. The results show that the change of mass, stiffness and damping for noisy conditions can be identified separately by using this approach even if these changes occur simultaneously.

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