An efficient procedure for optimal maintenance intervention in partially observable multi-component systems

With rapid advances in technology, many systems are becoming more complex, including ever-increasing numbers of components that are prone to failure. In most cases, it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained, one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study, we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures. •Examining partially observable multi-component systems.•Studying integrated maintenance and spare part selection decisions for such systems.•Devising a novel solution method that reduces the state space of the MDP formulation.•Enabling study of systems with large numbers of components.•Examining the benefit of using the optimal policy compared to naive heuristic policies.