Study and testing of wheelset wheel-flat identification features through axle-box vibration measurements

Abstract Early detection of wheelset defects is essential for ensuring railway safety. Wheelset condition monitoring can provide continuous information about the health of the system, thus avoiding time-consuming and expensive operations such as periodic inspections. This work deals with the study of railway wheelset wheel-flat identification features based on vibration signals from axle-box measurements. The aim is to obtain a simple and straightforward solution that can be easily implemented on a complete autonomous on-board sensor for wheelset defect prediction. Numerical simulations, by coupling a multi-body model of a coach with a new approximation of a wheel-flat model, were run in order to estimate the nature of the problem and the technical acquisition characteristics needed for a sensor node to be installed on real trains. Then, experimental campaigns were carried out on a wheelset test bench with defects artificially created to validate the presented methodology based on time domain feature extraction. A signal processing technique, which does not require the aid of any other hardware to obtain the revolution speed, is proposed. The methodology allows clear detection of wheel flats starting from 30 mm, especially at lower speeds. Even when considering the influence of wear, high defect conditions remain easily distinguishable.