Cost-optimal group maintenance strategy for metro wheels using degradation data and particle swarm optimization

Abstract

In urban rail transit systems, the wheelset is regarded as a critical component for ensuring the operational safety of metro vehicles. Its degradation behavior significantly influences vehicle stability and maintenance efficiency. Conventional maintenance strategies, typically based on periodic inspections or individual wheel conditions, often lead to low resource utilization and high maintenance costs, making them inadequate for the efficient management of large-scale fleets. To address these challenges, a group maintenance optimization method based on wheels degradation data is proposed. The degradation process is modeled using a gamma process, and a degradation rate function for each wheel is derived. An optimization model is then formulated to minimize the total maintenance cost, in which the optimal grouping scheme and the corresponding maintenance intervals are jointly determined using a particle swarm optimization algorithm. A simulation is performed using field-measured degradation data from 16 wheels of a metro vehicle. The results demonstrate that, compared with traditional individual maintenance strategies, the proposed method significantly reduces total maintenance costs and improves maintenance schedule coordination. Further sensitivity analysis reveals that the impact of the maintenance threshold is closely related to the heterogeneity in wheels degradation, highlighting the adaptability of the group maintenance strategy to diverse operational conditions.