A general approach to assessing SHM reliability considering sensor failures based on information theory

Wu, Wen; Cantero-Chinchilla, Sergio; Prescott, Darren; Remenyte-Prescott, Rasa; Chiachío, Manuel

Abstract

Structural health monitoring systems (SHM) involve implementing damage identification strategies to determine the health state of structures. However, it is important to pay close attention to the system degradation, especially the effect of sensor degradation on the SHM system reliability. This paper aims to formulate a general framework for evaluating SHM reliability that takes sensor failures into account. The framework involves modelling sensor network degradation processes using Petri nets (PNs) and calculating the expected information gain of the sensor network. The PNs allow for identifying the location and number of sensor failures. Kullback-Liebler (KL) divergence with Bayesian inversion is used to calculate the expected information loss due to sensor failure. Two case studies are used to illustrate the methodology: (i) a damage localization scheme using an ellipse-based time-of-flight (ToF) model and (ii) a damage identification scheme using a guided waves damage interaction model. The proposed framework is demonstrated by both numerical and physical experimental case studies. Whereas the case studies are specific to an ultrasonic guided wave monitoring system, the proposed approach is generic. The proposed model is able to predict the health condition state and utility of SHM, which can potentially help in constructing asset management models in various industries.

Citation

Wu, W., Cantero-Chinchilla, S., Prescott, D., Remenyte-Prescott, R., & Chiachío, M. (2024). A general approach to assessing SHM reliability considering sensor failures based on information theory. Reliability Engineering and System Safety, 250, Article 110267. https://doi.org/10.1016/j.ress.2024.110267

• Journal Article Type: Article
• Acceptance Date: Jun 7, 2024
• Online Publication Date: Jun 10, 2024
• Publication Date: 2024-10
• Deposit Date: Jun 13, 2024
• Publicly Available Date: Jun 13, 2024
• Journal: Reliability Engineering and System Safety
• Print ISSN: 0951-8320
• Electronic ISSN: 1879-0836
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 250
• Article Number: 110267
• DOI: https://doi.org/10.1016/j.ress.2024.110267
• Keywords: Monitoring system reliability, Structural health monitoring, Bayesian inverse problem, Kullback–Liebler divergence, Petri nets, Time of flight, Scattering coefficients
• Public URL: https://nottingham-repository.worktribe.com/output/36011700
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0951832024003399