The detection of track surface depression based on wavelet power spectrum

In order to overcome the existing shortcomings of the not-timely detection of track surface depression and the high detection cost, a method of detecting the track surface depression based on wavelet power spectrum is proposed. First of all, We install axle box vibration sensors on the operating vehicle to obtain the vertical and lateral vibration signals of axle box. Secondly, the vibration signal is filtered to reduce the influence of external factors such as wheel flat on the detection accuracy. Then the filtered signal is analyzed by wavelet scale to locate the fault characteristic frequency range of the signal. Finally, we calculate the wavelet power spectrum of the signal according to the fault characteristic frequency range, and determine whether there is a track surface depression when we compare the maximum of the wavelet power spectrum with the threshold. The simulation results show that the proposed method has a good application effect in the detection of track surface depression.