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