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

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.

Automated summary

This paper presents a joint optimization approach for inspection and condition-based mission abort policy in a partially observable safety-critical system. A hidden continuous-time Markov process is used to model the system deterioration, and the condition monitoring can only provide partial information about the system's hidden state. The joint inspection and mission abort policy is developed by employing a multi-variate Bayesian control approach. The posterior probability of the system being in the warning state is updated at each inspection using Bayes' rule. The mission is aborted and the rescue procedure is initiated when the posterior probability exceeds the control limit. The problem is formulated and solved within the framework of Markov decision process, aiming to minimize the expected total cost including inspection, mission failure, and system failure cost. The paper also presents some structural properties of the control limit and provides numerical examples to demonstrate the proposed model's effectiveness.