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

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

Reliability Engineering and System Safety, 2024, vol. 250, issue C

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–Leibler (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.

Keywords: Monitoring system reliability; Structural health monitoring; Bayesian inverse problem; Kullback–Leibler divergence; Petri nets; Time of flight; Scattering coefficients (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003399

DOI: 10.1016/j.ress.2024.110267

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