A new friction condition identification approach for wheel-rail interface

Abstract

In recent years, there has been an increasing interest in designing intelligent vehicles such that they can take necessary actions according to the environmental changes around them and they can inform decision makers about these changes. For safer and cheaper transport, dynamic modelling of these vehicles and identification of such changes in environment based on these models plays an important role. In this study, a sigma point Kalman filter-based scheme (i.e. joint unscented Kalman filter) is proposed to estimate maximum friction coefficient as a parameter in wheel-rail interface. This estimation scheme uses interpretation of lateral and yaw dynamic response of a wheelset to identify maximum friction coefficient. This joint unscented Kalman filter-based approach provides information about the friction conditions in wheel-rail interface without post-processing of estimated data.