The wheels are one of the most worn components on a train. When the wear is unacceptable, the re-profiling can restore the shape of the wheel flange with the cost of decreasing the wheel diameter. The decision of re-profiling has serious implications for the life span of wheels. In this paper, based on the analysis of the wear and re-profiling characteristics of metro wheels, a data-driven model of the relationship between the wheel diameter, the flange thickness, their wear rates, and the re-profiling gain is built for the wheels of Guangzhou Metro Line One. A (S dP, S dR) re-profiling strategy is proposed, where S dP is the wheel flange thickness threshold to trigger a preventive re-profiling and S dR is the wheel flange thickness after the preventive re-profiling. Then the Monte Carlo simulation model of the re-profiling strategy is described in this paper. To find out when a re-profiling should be performed in terms of the flange wear-out level and what values of the flange thickness should be brought to by re-profiling, the simulation results for optimizing the decision variables (S dP, S dR) of the re-profiling strategy are given in this paper. Those having longer life spans are listed as the preferred re-profiling strategies. The study in this paper reveals that the wear rate of the flange thickness is correlated with the flange thickness, while the diameter wear rate could be considered independent of the flange thickness in terms of the wheels of Guangzhou Metro Line One. On the other hand, based on the observation and analysis of an available sample set from Guangzhou Metro Line One, the re-profiling gain is dependent on the flange thickness before or after re-profiling. The preferred re-profiling strategies suggested by this study can increase the life span comparing with the existing re-profiling strategies based on the simulation. The models and methods presented in this paper could benefit both city metro companies and inter-city rail companies by prolonging the life span of rolling stock wheels. 
Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model