Industrial maintenance is undergoing a fundamental transformation as researchers demonstrate how Markov decision processes (MDPs) are redefining condition-based maintenance approaches. Traditional maintenance strategies that rely on scheduled replacements often waste resources or fail to prevent unexpected breakdowns, while condition-based maintenance enables interventions only when needed based on real-time system health monitoring. The research published in Frontiers of Engineering Management (https://doi.org/10.1007/s42524-024-4130-7) reveals that MDPs provide a powerful framework for modeling maintenance as a sequential decision-making problem where system states evolve stochastically and actions determine long-term outcomes.
Standard MDP-based condition-based maintenance models typically minimize lifetime maintenance costs, while variants such as risk-aware models also consider safety and reliability targets. For real-world applications where system states are only partially observable, partially observable Markov decision processes (POMDPs) handle uncertainty effectively, while semi-Markov decision processes accommodate irregular inspection and repair intervals. The complexity increases significantly for multi-component systems where dependencies such as shared loads, cascading failures, and economic coupling require higher-dimensional decision models.
To manage this computational complexity, researchers have applied approximate dynamic programming, linear programming relaxations, hierarchical decomposition, and policy iteration with state aggregation. Perhaps most promising is the emergence of reinforcement learning methods that can learn optimal maintenance strategies directly from data without requiring full system knowledge. This approach is particularly valuable for environments where system parameters cannot be fully defined in advance. However, challenges remain in data availability, stability, and convergence speed that must be addressed for practical implementation.
The research emphasizes that combining modeling, optimization, and learning offers strong potential for scalable condition-based maintenance systems. The implications extend across multiple industries 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. As 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 review suggests that future industrial maintenance platforms will integrate real-time equipment diagnostics with automated decision engines capable of continuously updating optimal policies. Such systems could support predictive planning across entire production networks, enabling safer, more economical, and more resilient industrial operations. The research provides a structured pathway for designing dynamic, cost-efficient maintenance policies that balance system reliability, operational continuity, and computational feasibility in modern industrial environments.
