Industrial maintenance is undergoing a fundamental transformation as researchers demonstrate how Markov decision processes (MDPs) are redefining condition-based maintenance approaches for complex systems. A comprehensive review published in Frontiers of Engineering Management reveals that MDPs provide a powerful framework for optimizing maintenance decisions under uncertainty, offering significant economic advantages over traditional time-based maintenance strategies.
Condition-based maintenance focuses on scheduling interventions according to the real-time health state of systems rather than following fixed schedules. However, optimizing CBM becomes challenging when dealing with complex degradation patterns, uncertain environments, and interacting components. The research, conducted by teams from Tianjin University, the ZJU-UIUC Institute at Zhejiang University, and the National University of Singapore, analyzes how MDPs and their variants are increasingly applied to support effective sequential maintenance decisions.
The study identifies MDPs as an ideal framework for modeling maintenance as a sequential decision-making problem where system states evolve stochastically and actions determine long-term outcomes. Standard MDP-based CBM models typically minimize lifetime maintenance costs, while variants such as risk-aware models also consider safety and reliability targets. To address real-world uncertainty, partially observable Markov decision processes (POMDPs) handle cases where system states are only partially observable, and semi-Markov decision processes allow for irregular inspection and repair intervals.
For multi-component systems, the review describes how dependencies—including shared loads, cascading failures, and economic coupling—significantly complicate optimization and often require higher-dimensional decision models. To manage computational complexity, researchers have applied approximate dynamic programming, linear programming relaxations, hierarchical decomposition, and policy iteration with state aggregation. The complete research findings are available at https://doi.org/10.1007/s42524-024-4130-7.
Reinforcement learning methods are emerging as particularly promising for learning optimal maintenance strategies directly from data without requiring full system knowledge. These approaches can adapt to environments where system parameters cannot be fully defined in advance, though challenges remain in data availability, stability, and convergence speed. The review emphasizes that combining modeling, optimization, and learning offers strong potential for scalable CBM implementation across various industries.
The implications of this research extend across multiple sectors where reliability is essential, including manufacturing, transportation, power infrastructure, aerospace, and offshore energy. More adaptive maintenance strategies derived from MDPs and reinforcement learning can reduce unnecessary downtime, lower operational costs, and prevent safety-critical failures. The authors suggest that future industrial maintenance platforms will integrate real-time equipment diagnostics with automated decision engines capable of continuously updating optimal policies.
This advancement matters because traditional maintenance approaches often waste resources or fail to prevent unexpected breakdowns, while CBM enables maintenance only when needed. As industrial systems become more complex and sensor data more abundant, the ability to integrate multi-source information into maintenance planning becomes increasingly critical for operational efficiency and safety. The research provides a structured pathway for designing dynamic, cost-efficient maintenance policies that balance system reliability, operational continuity, and computational feasibility.
The transition to MDP-based maintenance strategies represents a significant step toward predictive planning across entire production networks, enabling safer, more economical, and more resilient industrial operations. This approach aligns well with real operational needs by supporting dynamic, state-based decision-making under uncertainty, potentially transforming how industries manage equipment reliability and maintenance costs in increasingly complex technological environments.
