Decentralized Maintenance for Multistate Systems With Heterogeneous Components

This study considers the decentralized maintenance of a multistate system (MSS) with low-priority components (LPCs) and high- priority components (HPCs). By introducing imperfect observations of the state, the MSS can be modeled as a partially observable Markov decision process. We propose an (m, N) maintenance policy, where it is considered that the MSS has failed when an HPC fails or when the number of failed LPCs reaches m. In contrast to a centralized maintenance mode, two maintenance teams conduct reliability evaluations and maintenance actions. One team employs the Markov method to predict the trends in the deterioration of the components. The other team estimates the status of the MSS based on the sample data, which are stochastically related to the condition of the system. The different teams may have different maintenance costs and effects, and either maintenance team can be selected based on the system's status. We discuss in detail how to arrange the maintenance teams in order to obtain the lowest expected cost rate with a guarantee of system reliability. Illustrative numerical examples are provided to show the significant cost savings under decentralized maintenance compared with centralized maintenance due to either lower expenditure or shorter time requirements.