Selective maintenance and inspection optimization for partially observable systems: An interactively sequential decision framework

Selective maintenance is an important condition-based maintenance strategy for multi-component systems, where optimal maintenance actions are identified to maximize the success likelihood of subsequent missions. Most of the existing works on selective maintenance assumed that after each mission, the components' states can be precisely known without additional efforts. In engineering scenarios, the states of the components in a system need to be revealed by inspections that are usually inaccurate. Inspection activities also consume the limited resources shared with maintenance activities. We, thus, put forth a novel decision framework for selective maintenance of partially observable systems with which maintenance and inspection activities will be scheduled in a holistic and interactively sequential manner. As the components' states are partially observable and the remaining resources are fully observable, we formulate a finite-horizon Mixed Observability Markov Decision Process (MOMDP) model to support the optimization. In the MOMDP model, both maintenance and inspection actions can be interactively and sequentially planned based on the distributions of components' states and the remaining resources. To improve the solution efficiency of the MOMDP model, we customize a Deep Value Network (DVN) algorithm in which the maximum mission success probability is approximated. A five-component system and a real-world multi-state coal transportation system are used to demonstrate the effectiveness of the proposed method. It is shown that the probability of the system successfully completing the next mission can be significantly increased by taking inspections into account. The results also demonstrate the computational efficiency of the customized DVN algorithm.