A Deep Reinforcement Learning Approach for Condition-Based Maintenance of Multi-Unit Systems with Complex Interdependencies

We investigate the decision-making problem of condition-based maintenance for multi-unit series systems with non-identical units subject to internal deterioration and external shocks.Two types of interdependencies are considered: (1) stochastic and economic dependencies among units, where the deterioration of one unit affects others, and simultaneous maintenance reduces costs; and (2) interactions between internal deterioration and external shocks, where accumulated deterioration increases susceptibility to shocks, and shocks accelerate deterioration.These interdependencies and the high-dimensional nature of the decision-making problem make traditional algorithms, such as value iteration and policy iteration, computationally infeasible for large-scale systems.To address this challenge, a deep reinforcement learning (DRL) algorithm is adopted.The DRL-based approach effectively captures these interdependencies and derives maintenance policies, demonstrating its adaptability to real-world systems with complex dependencies and high-dimensional state spaces.Furthermore, the DRL-based policy achieved lower maintenance costs and downtime due to failures than the alternative policy, which optimizes individual unit policies.This study contributes to advancing the application of DRL in reliability engineering and maintenance management.The study concludes with a discussion of the limitations of the proposed policies and suggestions for future research.