No abstract

Wheel maintenance is a complex process whose costs can be reduced with good planning. One of the main difficulties is the prediction of a worn wheel profile shape on a train. With existing modelling approaches, it is possible to predict a worn wheel profile quickly and accurately for a unique operating situation. For varying operating scenarios, it is a more time-consuming process and often less accurate manner because so many, sometimes even unknown, input data are needed. With the new hybrid approach developed in this work, it is possible to combine the advantages of both approaches (fast, accurate, varying operating scenarios). The hybrid approach builds on historical data sets of two trains in combination with multi-body dynamic simulations. In these simulations, two different wear models have been used, one based on the maximum shear stress, the other on the wear number in the contact point. The wear model approach based on the maximum contact shear stress was confirmed as accurate through application of the hybrid model and validation using real track measurements. This will help to improve the prediction of maintenance intervals and, thus, to reduce the costs.