Predicting railway wheel wear by calibrating existing wear models: Principle and application

The accuracy of physics-based wheel wear prediction approaches depends not only on the vehicle-track dynamics model but more importantly on the wear model. The currently available wear models, however, were developed for specific wheel/rail materials and under fixed operational conditions, and they need to be calibrated when used in new operational conditions. In this paper we introduce a data-driven approach into the physics-based approach to automatically calibrate the existing wear model so that it can be extended to a new operational scenario. Specifically, based on a one-year follow-up measurement data on the wheel profile evolution of a metro train commuting on a Guangdong metro line, we introduce radial basis function (RBF) and particle swarm optimization (PSO) to determine the boundary of each wear regime and the wear coefficient for each wear regime in the Archard-local model. Finally, we present a calibrated Archard-local model and then apply it to two cases. One application case shows that the calibrated model can be used for long-term wear prediction and another case shows that the calibrated model can be used to roughly estimate the wear evolution over the tread region of vehicles operating on other China metro lines.