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Low adhesion in the wheel/rail contact can be caused by small amounts of water combining with iron oxides. This happens in light rain or at dew point. In small-scale tests, ultra-low adhesion (≤0.05) has not been maintained. The aim here was to see if the mechanism could be realized at a larger scale. Sustained ultra-low adhesion was achieved when water was applied constantly to the wheel/rail contact at a rate of 25µL/s. In these conditions wear debris and oxide was clearly visible in the contact band. Creep force data has been generated that can now be used to inform wheel/rail contact models and multi-body dynamics simulations of train behaviour with a view to developing mitigation.

Monitoring the conditions of railway vehicle systems plays an important role in the maintenance of safety and performance of railway vehicles. Rolling radius is one of the properties that should be monitored continuously for the predictive maintenance of a railway vehicle since it changes with time due to wheel wear. In this study, a model-based condition monitoring methodology, which is based on an unscented Kalman filter, is proposed. The model includes the torsional dynamics of an independently rotating tram wheel with a traction motor and a contact model. The rolling radius is estimated by considering the traction effort of the motor and the angular velocity measurements. The proposed methodology is tested on a tram wheel test stand (roller rig), which has a wheel on roller configuration. First, a mathematical model is validated by the measurements taken from the test stand. Second, the unscented Kalman filter is applied as a parameter estimator. The results demonstrate that the proposed scheme is a promising option to be used in the predictive condition monitoring of the wheel profile for traction vehicles.