Self-Diagnosis of Acoustic Emission Sensors: Electromechanical Impedance-Based Damage Detection

Abstract

The reliability and functionality of Acoustic Emission (AE) sensors is essential for their role in non-destructive testing (NDT) and structural health monitoring (SHM) systems. Conventional field-verification methods, such as the pencil lead break test (Hsu- Nielsen source), require manual interaction and are susceptible to external influences from the test structure. Furthermore, human interaction in close proximity to the sensor location requires a considerable effort when monitoring structures such as bridges or wind turbines. These shortcomings often necessitate additional verification under laboratory conditions, thereby underscoring the requirement for a more efficient and reliable approach to the verification of AE sensor performance in the field. A promising solution is the analysis of the electromechanical impedance (EMI) spectrum of AE sensors. This method facilitates remote and automated self-diagnosis by capturing the mechanical and electrical characteristics of the sensor, as well as its interactions with the coupling layer and surrounding structure. This paper presents the experimental investigation of the detection of mechanical damage in AE sensors using the EMI spectrum. The damage was induced systematically by controlled drop-down tests, which simulated realistic degradation scenarios. The experimental results are further validated by comparison with an established wavebased verification setup described in the guideline for the verification of sensors and their coupling in laboratories by the German society for non-destructive testing, which is a widely accepted standard for AE sensor verification under laboratory conditions. The comparison of EMI-based self-diagnosis with the wave-based SE02 verification method demonstrates that the EMI is capable of reliably detecting sensor damage. This provides evidence that the EMI-based self-diagnosis is a viable alternative to traditional manual and laboratory-based methods. Moreover, this approach offers several significant advantages, including automation, reduced reliance on human input, and suitability for deployment in field conditions. By addressing common damage scenarios, such as mechanical degradation from impacts, this work contributes to the development of robust self-diagnosis capabilities for AE sensors.