Optimal joint inspection and mission abort policy for a partially observable system

• Published in: Reliability engineering & system safety Vol. 229; p. 108870
• Main Authors: Cheng, Guoqing, Li, Ling, Shangguan, Chunxia, Yang, Nan, Jiang, Bo, Tao, Ningrong
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.01.2023; Elsevier BV
• Subjects: Bayesian analysis; Condition monitoring; Conditional probability; Inspection; Markov decision process; Markov processes; Mission abort; Mission reliability; Optimization; Partially observable system; Reliability engineering; Safety critical; System survivability; Partially observable system; Markov decision process; Mission reliability; Mission abort; System survivability
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108870

•A joint inspection and mission abort policy is developed for a partially observable system.•Dynamic condition based mission abort policy are optimized.•The structural properties of the optimal abort policy are investigated.•The detailed comparison between the proposed policy and the fixed threshold policy is conducted. his paper presents a joint optimization of inspection and condition-based mission abort policy for a partially observable safety-critical system. The system deterioration is modeled by a hidden continuous-time Markov process. Condition monitoring can only reveal partial information about the hidden state of the system. A joint inspection and mission abort policy is developed by using multi-variate Bayesian control approach. The posterior probability of the system being in warning state is updated at each inspection by Bayes’ rule. The mission is aborted and the rescue procedure is initiated once the posterior probability exceeds the control limit. The problem is formulated and solved within the framework of Markov decision process which aims to minimize the expected total cost including inspection, mission failure and system failure cost. Some structural properties of the control limit are presented. Finally, numerical examples are provided to demonstrate the model.