A general approach for evaluating SHM system reliability with sensor failures

Wu, Wen; Cantero-Chinchilla, Sergio; Remenyte-Prescot, Rasa; Prescott, Darren; Saleh, Ali; Chiachio Ruano, Manuel; Chronopoulos, Dimitrios

Abstract

Structural health monitoring (SHM) systems involve implementing damage identification strategies to determine health states of structures. However, it is important to pay close attention to the degradation of the SHM itself, especially the effect of sensor degradation on the reliability of the SHM. This paper aims to formulate a general framework for evaluating SHM reliability that takes sensor failures into account. The framework involves modelling the degradation process of sensor network using Petri nets (PNs) and calculating the expected information gain/loss of the sensor network based on Bayesian inverse approach. The PNs allow for taking account of the sensor location and the number of sensor failures. A Bayesian inverse procedure is used to calculate the expected information loss due to sensor failures, which integrates a damage localization and a damage identification scheme to update information about model parameters. The proposed framework is demonstrated for a plate with a part-thickness hole, monitored by an ultrasonic guided wave monitoring system. The given framework could be adapted to other monitoring systems. The proposed model is able to predict the health condition state of the SHM, which can be included in asset management models for various industries.

Citation

Wu, W., Cantero-Chinchilla, S., Remenyte-Prescot, R., Prescott, D., Saleh, A., Chiachio Ruano, M., & Chronopoulos, D. (2023, July). A general approach for evaluating SHM system reliability with sensor failures. Presented at 12th IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR), Nottingham, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR)
• Start Date: Jul 4, 2023
• End Date: Jul 6, 2023
• Acceptance Date: Jun 12, 2023
• Online Publication Date: Nov 1, 2023
• Publication Date: Jul 4, 2023
• Deposit Date: Jan 8, 2024
• Publicly Available Date: Jan 8, 2024
• Public URL: https://nottingham-repository.worktribe.com/output/29542200
• Publisher URL: https://ima.org.uk/proceedings-of-the-12th-ima-international-conference-on-modelling-in-industrial-maintenance-and-reliability/