Reinforcement Learning in Condition-Based Maintenance: A Survey

Abstract

This literature review examines the convergence of Reinforcement Learning (RL) and Condition-Based Maintenance (CBM), emphasizing the transformative impact of RL methodologies on maintenance decision-making in complex industrial settings. By integrating insights from a diverse array of studies, the review critically assesses the use of various RL techniques such as Q-learning, deep reinforcement learning, and policy gradient approaches in forecasting equipment failures, optimizing maintenance schedules, and reducing operational downtime. It outlines the shift from conventional, rule-based maintenance practices to adaptive, data-driven strategies that exploit real-time sensor data and probabilistic modeling. Key challenges highlighted include computational complexity, the extensive training data requirements, and the integration of RL models into existing industrial frameworks. Furthermore, the review explores literature on CBM within multi-component systems, where prevalent approaches include numerical analyses, Markov Decision Processes (MDPs), and case studies, all of which demonstrate notable cost reductions and decreased downtime. Relevant studies were identified through searches on databases such as Google Scholar, Scopus, and Web of Science. Overall, this review provides a comprehensive analysis of the current state and prospects of employing reinforcement learning in conditionbased maintenance, offering valuable insights for both academic researchers and industry practitioners.