Adhesion level identification in wheel-rail contact using deep neural networks

Abstract

Robust and accurate adhesion level identification is crucial for proper operation of railway vehicle. It is necessary for braking and traction forces characterization, development of maintenance strategies, wheel-rail wear predictions and development of robust onboard health monitoring systems. Adhesion being the function of many uncertain parameters is difficult to model, whereas data driven algorithms such as Deep Neural networks (DNNs) are very good at mapping a nonlinear function from cause to effect. In this research a solid axle Wheel-set was modeled along with different adhesion conditions and a dataset was prepared for the training of DNNs in Python. Furthermore, it explored the potential of DNNs and various data driven algorithms on our noisy sequential dataset for classification task and achieved 91% accuracy in identification of adhesion condition with our final model.