Condition Monitoring Approaches To Estimating Wheel-Rail Profile

The wheel and rail interaction is the main influence on the dynamic response of a rail vehicle. Any changes in the wheel and rail will change the overall response of the vehicle. The condition monitoring challenge is to interpret these changes into useful condition information. This paper presents the results from initial feasibility studies into model-based condition monitoring at the wheel-rail interface applied to estimating the wheel-rail profile estimation. A number of approaches are presented, based around a Kalman Filter method and least squares methods, applied to a linearised simulation model that included a nonlinear conicity function. The function was successfully estimated using a Kalman Filter that included self-updating information about the shape of the conicity function, and by a piecewise cubic least squares approach.