This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization.
Cao et al. (Tue,) studied this question.