Data-Driven Wheel Wear Modeling and Reprofiling Strategy Optimization for Metro Systems

Abstract

With the rapid developments in metro systems worldwide, more research concerning optimization of maintenance actions is needed, because the availability and service state of a metro system directly influences the daily activity of a city and its people. In particular, the prediction of wear and maintenance optimization of wheels is significant. Maintenance costs for a rail track subsystem represent more than half the total maintenance costs for a metro line. A hard rail–soft wheel compromise extends the life of the rails and increases the wheel replacement frequency with economic benefits. An improved strategy for predicting and maintaining wheel wear will allow agencies to improve reliability, enhance safety, and maximize wheel life while minimizing relevant costs. In this study, historical data are used to analyze wheel wear curves, and the flange thickness and wheel diameter are identified as the most important profile parameters. A new data-driven model of wheel wear trends is given for variations in wheel diameter and flange thickness. An approach for optimizing the wheel reprofiling strategy is based on this model and determines the optimum reprofiling point that maximizes wheel life while minimizing relevant costs. An initial case study on the Shanghai, China, metro network shows that the proposed approach can provide a reasonable solution for optimization of the reprofiling strategy.