System Maintenance Optimization Under Structural Dependency: A Dynamic Grouping Approach

Abstract

Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.