Systemic Condition-Based Maintenance Optimization Under Inspection Uncertainties: A Customized Multiagent Reinforcement Learning Approach

Condition-based maintenance (CBM) powered by inspection/monitoring technology is crucial to guarantee safety and economical operations of various industrial assets. The implementation of prevailing CBM procedures for large-scale heterogeneous systems, however, is increasingly challenged by model intractability and computational cost stemming from the synergistic effect of information completeness and structure complexity. In this article, we innovatively devises a tractable CBM model for multicomponent continuously degrading systems under nonperfect inspection information, which is applicable to heterogeneous system structure and arbitrary hierarchical maintenance actions. The maintenance optimization problem of interest constitutes a continuous-state partially observable Markov-decision process applicable to heterogeneous system structures. A series of structure properties associated with systematic conditional reliability and accessibility of optimal solution are established, following which a multiagent reinforcement learning model governed by partial-independent parameter-sharing mechanism is employed to allow for solution search under continuous state–action space. A customized proximal policy algorithm is then leveraged to facilitate efficient agent training by diminishing the cure of dimension. Comparative experiments conducted on train wheel treads verify the superior model performance over cost control and computational efficiency improvement.