As a modern tool for the implementation of digital maintenance strategies, the digital twin is considered to be very promising in the industry in general and in the rail vehicle industry in particular. Wheelset maintenance is one of the most difficult and expensive maintenance tasks on a rail vehicle and is therefore of particular interest for implementation as a digital twin. In a joint project, the TU Berlin, the RWTH Aachen and the DB AG are developing a digital twin of the 429.1 series for the prediction of wheel profile wear. The goal of the project is to create a tool that automatically calculates tread wear based on a vehicle's operating data - both in real time as a digital twin and as a prediction for the future.
Wheel profile prediction is based on multi-body simulations (MBS), which are automatically created, executed and evaluated as a function of the planned or actual distance driven. The contact mechanics results of the multi-body simulations provide the input for the automated calculation of the wear volume. A comprehensive set of wheel profile measurements is used to adjust and validate the wear. A proof of concept has already been successfully developed, which will be further developed in the course of the project into an operational digital twin by taking into account additional contact-mechanical influencing variables and numerous improvements.