On the On–Board Random Vibration–Based Detection of Hollow Worn Wheels in Operating Railway Vehicles

Abstract

The feasibility of on–board hollow worn wheel detection in railway vehicles moving under different speeds based on bogie random vibration signals, is for first time investigated in this study. Towards this end, two unsupervised Statistical Time Series (STS) methods which are founded on Multiple Models (MM) for the representation of the vehicle partial dynamics are employed: The Unsupervised–MM–Power Spectral Density (U–MM–PSD) and the Unsupervised–MM–AutoRegressive (U–MM–AR). The methods’ assessment is achieved based on thousands of test cases from field tests with an Athens Metro railway vehicle possessing early stage hollow or reprofiled wheels under two nominal speeds (70 or 80 km/h). The results indicate both methods’ excellent performance based on vibration measurements on the lateral direction, as well as the increased sensitivity of the U–MM–AR method to the detection of hollow worn wheels when vertical vibration signals are used.