Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

Yunfei Zhao, Wei Gao and Carol Smidts

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. In this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

Keywords: Sequential Bayesian inference; Importance sampling; Forward–backward algorithm; Model parameter inference; Hidden Markov model; Multi-state system; Multi-source evidence; Information fusion; Nuclear power plant; Maintenance optimization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002039

DOI: 10.1016/j.ress.2021.107662

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