Comparative analysis of explainable artificial intelligence methods in sensor fault diagnosis for vibration-based structural health monitoring

Reliable structural health monitoring (SHM) depends on accurate analysis of sensor data as well as on fault-free operation of sensors collecting the data, which is usually ensured via sensor fault diagnosis (FD). Artificial intelligence (AI) models have shown promising po-tential for sensor FD, mainly exploiting the pattern-learning capabilities in the sensor data. However, the logic behind AI predictions is often opaque, limiting practitioner trust and adop-tion in autonomous SHM systems. To counteract the aforementioned limitation, explainable artificial intelligence (XAI) has emerged, aiming to improve transparency of AI models by in-dicating which aspects of input sensor data influence AI-derived predictions. Nevertheless, XAI applications in SHM remain rare, and most studies focus on the feasibility of applying XAI for SHM rather than practical aspects, such as the reliability and robustness of XAI explanations. This study investigates explanation robustness in XAI-based sensor FD for vibration-based SHM through a comparative analysis of different XAI methods. In particular, AI models are trained on sensor data, and synthetic faults are injected in the sensor data, simulating realistic sensor faults. Explanation robustness is analyzed using attribution consistency (Spearman rank correlation coefficient), and attribution stability (coefficient of variation) across multiple fault scenarios, comparing gradient-based and perturbation-based XAI methods. Validation tests are presented using acceleration response data, collected from a pedestrian bridge. The vali-dation results show that gradient-based methods demonstrate higher explanation robustness than perturbation-based methods when explaining vibration-based sensor fault patterns. Fu-ture work will extend the comparative analysis to distinguish between anomalies caused by sensor faults and anomalies arising from changes in structural dynamic behavior.

Content

Subjects

Structural health monitoring (SHM)

explainable artificial intelligence (XAI)

sensor fault diagnosis

vibration-based SHM

explanation robustness

DDC Class

600: Technology

Funding References

Funding(s)

Dezentrale digitale Zwillinge für das Bauwerksmonitoring

Aufbau internationaler Kooperation zum Thema "Digitale Immunisierung baulicher Infrastruktur"