Characteristic Analysis Of Monitoring Data Of High-Speed Train Based On Wavelet Coefficients

With the speed of high-speed train continues to improve, the security state of train in the process of long-term service will change and make the train safety warning and health maintenance poses special challenges. During the high-speed train running the sensors monitor a large number of vibration data, how to identify the train running status based on tracking and monitoring data and make security state assessment are critical. Vibration monitoring data is nonlinear and non-stationary signals, wavelet analysis has good time-frequency localization advantages. It is widely used in the signal characteristics analysis. Therefore, in this thesis, analyzing characteristic of security state assessment data of high-speed train based on wavelet analysis, and studying the four states about normal state of train, air spring loss of gas, anti-hunting demolition and lateral damper demolition.Based on continuous wavelet transform, a feature extraction method of scale-wavelet power was proposed. Along the decomposition of wavelet scale, this method calculates the energy of different scales, get energy distribution characteristics of different wavelet scale of the working condition of four types. Based on wavelet packet transform, it use feature extraction algorithm of kurtosis combined with wavelet packet, in order to analyze the spatial distribution of the signal’s kurtosis. According to the change of frequency band wavelet packet kurtosis to train different status monitoring can be realized. Four kinds of operating mode under different speed and different sensor output signal showed different patterns. Finally, the train states were recognized by using support vector machines, and results show that the two kinds of feature extraction algorithms are effective.For the lateral displacement signal has low frequency, combining the wavelet packet to Spectral kurtosis method to extract the characteristics of each state. After the signal wavelet decomposition, calculate the spectral kurtosis value of the scales. High-speed train’s state parameter gradients and multiple faults were analyzed based on the scale-wavelet power and wavelet packet kurtosis moment. The distribution characteristic of state process from normal gradient to three kinds of entirely fault was analyzed and also discussed the multiple faults’characteristics were embodied or retained the characteristic of single fault.This work was supported by National Natural Science Foundation of China.(No.61134002).