On-board vibration-based remaining useful life estimation of hollow worn railway vehicle wheels

Abstract

Abstract The feasibility of on-board remaining useful life (RUL) estimation of hollow worn railway vehicle wheels under varying travelling speed using vibration signals from the vehicle’s bogie, is for the first time explored. A variant of the Multiple Model Power Spectral Density (MM-PSD) method is employed, treating RUL estimation as a multi-class classification problem, where each class corresponds to a specific condition of hollow worn wheels associated with an empirical remaining mileage. The method classifies the current, unknown, condition of hollow worn wheels to one of the available by examining the vehicular dynamics similarity among them through the amplitude of the sharp periodic valleys (antiresonances) induced due to the Wheelbase Filtering (WF) effect. The vibration signals are obtained from Monte Carlo experiments based on the high-fidelity SIMPACK software, while the remarkable performance of the method is demonstrated via thousands of inspection cases.