Feature Anal Ysis Of Safety State Evaluation High-Speed Trains Data Based On Fract Ional Fourier Transform

With the rapid development of China high-speed train technology, the speed of the train vehicles are constantly improved, and the increase of train speed also worsen the train dynamic environments, increases the wheel-rail forces of train, the the vibration of various components and hunting oscilations. At the same time as the train service time increases, the train components wear out more rapidly resulting in the rapid disintegration of its performance parameters, seriously affected the quality of train running. How to extract effective features of the high-speed trains monitoring data, and quickly and accurately estimate the performance of high-speed train security service, has become a serious problem in the field of security early warning of high-speed trains.High-speed train monitoring data are nonlinear and non-stationary,fractional Fourier transform (Fractional Fourier Transform, FRFT) is an extended form of the Fourier transform, simultaneously characterize signals in time domain and frequency domain, widely used in the field of non-stationary signal processing field. This paper uses the fractional Fourier transform to extract signal fractional-domain features, and estimate the status of the train running by using fractional-domain features, the main research work are as follows:This paper analyze the advantages and disadvantages of the short-time Fourier transform, wavelet transform, Wigner-Willie distribution (WVD) and Randon-Wigner distribution and other classic time-frequency analysis, and study the basic principles and theoretical knowledge of the new time-frequency analysis method fractional Fourier transform.Extraction of the fractional-domain features of the high-speed train monitoring data, firstly transform high-speed train monitoring data to fractional Fourier space, then using the fractional Fourier space data to get lateral projection, which shows signal maximum peak in different fractional space, finally calculat the statistical features of the peak curve.Study the structure of the high-speed train bogie and related dynamics analysis. Using fractional Fourier transform to extract the features of high-speed train monitoring data, to study the distribution of different speed of simulation data each working condition. Simulate the data of all channels a single working condition and the use the support vector machines to classify working condition of four kinds of single. Compile the statistics of channels of a more good classification effect by analyzing the recognition rate of the four conditions of different channels of different speeds.Simulate and get the statistics of test data, further verify the effectiveness of the channel and features. By comparing the two kinds single working condition and the mixed condition on the lateral and vertical features distribution, exploring the association between multiple fault condition and single working condition. The simulation of parameters gradient conditions shows the features distribution of train working condition from the original train to the three complete failure processing.In summary, this paper uses the fractional Fourier transform to simulate high-speed train different running status data, the simulation and analysis result proves the feasibility and effectiveness of fractional domain features on high-speed train failure recognition.