A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation

Predicting friction at the wheel-rail interface is a key problem in the rail industry. Current forecasts give regional-level predictions, however, it is well known that friction conditions can change dramatically over a few hundred meters. In this study, we aimed to produce a proof-of-concept friction prediction tool which could be used on trains to give an indication of the limiting friction present at a precise location. To this end, field data including temperature, humidity, friction, and images were collected. These were used to fit a statistical model including effects of local environmental conditions, surroundings, and railhead state. The model predicted the friction well with an R-2 of 0.97, falling to 0.96 for naive models in cross validation. With images and environmental data collected on a train, a real-time friction measurement would be possible.