Rail Corrugation Detection of High‐Speed Railway Using Wheel Dynamic Responses

Abstract

Rail corrugation often occurs on the high‐speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle‐track coupling model considering the rail corrugation of high‐speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high‐frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.