An anti-disturbance method for on-board detection of early wheel polygonal wear by weighted angle-synchronous moving average

Abstract

Wheel polygonal wear (WPW) on-board detection based on axle-box acceleration (ABA) is a critical instrument for railway wheel maintenance. However, early WPW on-board detection under typical disturbances such as rail defects is still difficult to achieve. To face this challenge, this study proposes a weighted angle-synchronous moving average (WASMA) anti-disturbance filtering for WPW on-board detection. Starting with the gear mesh component, the accurate instantaneous phase of the wheelset is calculated using a phase demodulation and time–frequency ridges combination. The ABA is then filtered using WASMA to suppress typical disturbances while revealing the features of early WPW. Simulation and field tests were carried out to verify the proposed detection method. The results demonstrated that the proposed method effectively filters out typical disturbances, such as random track irregularity, sleeper passing impact, and structural resonance, reducing the misdiagnosis of early WPW.