Multi-agent deep reinforcement learning-based maintenance optimization for multi-dependent component systems

Abstract

Manufacturing systems consist of a set of interdependent components. However, addressing the dependence between these components remains a challenge in both maintenance modeling and the optimization process. In this paper, we propose a multi-agent deep reinforcement learning-based maintenance approach for a manufacturing system, taking into consideration both stochastic and economic dependencies between components. In this manner, we introduce a novel state interactions model, suggesting that the degradation state of one component may influence the degradation process of others. Subsequently, a maintenance planning approach based on multi-agent deep reinforcement learning is developed to optimize maintenance decisions in both fully and partially observed states. The deployed multi-agent deep reinforcement algorithm, specifically Weighted QMIX, ensures scalability and efficient consideration of state interactions and economic dependencies between components. The feasibility and performance of the proposed maintenance approach are investigated through various numerical studies. When compared to traditional maintenance approaches, such as value iteration method, Dueling Double Deep Q Network, and Multi-Agent Deep Q Network, our proposed approach consistently demonstrates superior results.