High-speed railway wheel polygon detection framework using improved frequency domain integration

Abstract

Timely monitoring of wheel polygon is of great importance for the formulation of railway wheel maintenance strategies. In this study, a novel data-driven method for onboard and quantitative detection of wheel polygon is presented. First, the axle box acceleration (ABA) signal preprocessing method and stationarity test are introduced to select the relatively stationary signal from the measured data of ABA. Next, an iterative algorithm is developed to accurately extract the quasi-stationary ABA signals, representing each wheel rotation period. Then, an improved frequency domain integration method is developed to quantitatively capture the orders and roughness levels of the wheel polygon. Finally, the effectiveness and superiority of the proposed method is verified using the field-measured data of ABA and the wheel polygon in one cycle of wheel re-profiling. The results show that the proposed method can quantitatively capture the dominant characteristics of single- and multi-orders wheel polygons at different operating mileages with minimum and maximum absolute errors of 0.04 dB re 1 µm and −2.33 dB re 1 µm, respectively. The comparative analysis demonstrates that the proposed method outperforms the traditional time and frequency domain integration algorithms in the detailed characterisation of wheel polygon roughness levels.