Rolling-horizon optimization of dynamic grouping maintenance for series systems

Multi-component series systems require scientifically formulated group maintenance strategies due to the intricate interdependencies among components. To address the limitations of static grouping methods, which lack the capacity to adapt dynamically in the absence of real-time system status information, this study proposes a dynamic group maintenance optimization model based on a rolling horizon framework. The model utilizes real-time monitoring data obtained from Prognostics and Health Management (PHM) systems to construct continuously updated maintenance planning windows. Particle Swarm Optimization (PSO) is applied to determine the optimal preventive maintenance timing for individual components, while Dynamic Programming (DP) is employed to identify the globally optimal maintenance combination within each planning window. This integrated approach ensures cost minimization in dynamic operational environments. Case studies validate the model's effectiveness and highlight its practical applicability in group maintenance scenarios.