Dynamic Scheduling of Intelligent Group Maintenance Planning under Usage Availability Constraint

Maintenance, particularly preventive maintenance, is a crucial measure to ensure the operational reliability, availability, and profitability of complex industrial systems such as nuclear asset, wind turbines, railway trains, etc. Powered by the continuous advancement of sensor technology, condition-based group maintenance has become available to enhance the execution efficiency and accuracy of maintenance plans. The majority of existing group maintenance plans are static, which require the prescheduling of maintenance sequences within fixed windows and, thus, cannot fully utilize real-time health information to ensure decision-making responsiveness. To address this problem, this paper proposes an intelligent group maintenance framework that is capable of dynamically and iteratively updating all component health information. A two-stage analytical maintenance model was formulated to capture the comprehensive impact of scheduled maintenance and opportunistic maintenance through failure analyses of both degradation and lifetime components. The penalty functions for advancing or postponing maintenance were calculated based on the real-time state and age information of each component in arbitrary groups, and the subsequent grouping of the time and sequence of components to be repaired were iteratively updated. A lifetime maintenance cost model was formulated and optimized under a usage availability constraint through the sequential dynamic programming of group sequences. Numerical experiments demonstrated the superior performance of the proposed approach in cost control and availability insurance compared with conventional static and periodic maintenance approaches.