Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring

Although Bayesian approaches have been utilized in engineering systems for health prognostics, very little work has been done using Bayesian methods for fault prediction of systems under multiple attributes. To address this issue, in this paper a novel multi attribute Bayesian control chart is presented for predicting failures of hidden-state systems by jointly considering two performance measures of system operation. The system actual status is represented by a three-state multivariate hidden stochastic process with a normal state, an abnormal state, and a failure state. The working states are unobservable and failure state is observable. Based on the built hidden-state model, a fault prediction scheme integrating both system availability and cost objectives is constructed via a multi-attribute Bayesian control chart to monitor and predict impending risks of the operational systems. The Bayesian control chart alarms when the probability of impending risks reaches a certain control limit, which is optimized and determined by a computational algorithm developed in a semi-Markov decision process framework. The proposed fault prediction scheme provides an appearing feature to jointly consider multiple attributes for hidden-state systems. A real case study of mechanical generators is presented and a comparison with other Bayesian and non-Bayesian methods is also given, which demonstrates the effectiveness and superiority of the proposed approach. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

A novel multi-attribute Bayesian control chart is proposed in this paper to predict failures of hidden-state systems by jointly considering two performance measures of system operation. The fault prediction scheme integrates system availability and cost objectives to monitor and predict impending risks using a computational algorithm developed in a semi-Markov decision process framework, demonstrating the effectiveness and superiority of the approach.