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 trans- formative impact of RL methodologies on maintenance decision-making in com- plex 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 condition-based maintenance offering valuable insights for both academic researchers and industry practitioners. © 2025 Elsevier B.V. All rights reserved.