Opportunistic preventive maintenance strategy of a multi-component system with hierarchical structure by simulation and evaluation

Equipment usually consists of many components arranged in hierarchical structure. In order to achieve efficient maintenance strategy, the system hierarchy should be taken into account. In this paper, we first give a nomenclature to describe a system composed of multiple non-identical components in a hierarchical structure, the system for an age-based and an opportunistic preventive maintenance strategies is modeled by using a Markov Decision Process (MDP). Then, near-optimal policies are found through the SARSA(λ) algorithm from Reinforcement Learning (RL), where the expected discounted cost is minimized. Simulation experiments to compare near-optimal policies obtained by SARSA(λ) are performed for both strategies with corrective maintenance and with age-based preventive maintenance policy obtained from renewal reward theory. We show that the proposed opportunistic preventive maintenance outperforms other strategies.