Research On Service Process Monitoring And Fault Diagnosis System Of Train Bogie Bearing

Axlebox bearing is one of the key components of train bogie. The condition of the axlebox bearing has great influence on rail traffic security. A real time and effective axlebox bearing fault diagnosis method could avoid traffic accidents and reduce operation costs. By this way, we can adopt condition-based maintenance instead of preventive maintenance which is now being widely adopted. This thesis takes service process monitoring and fault diagnosis system as the research object, studies the signal acquisition, signal processing analysis, fault feature extraction and pattern recognition.Signal acquisition implements the function of packing up vibration and speed signal. Anti-aliasing filter based on second order chebyshev filter and butterworth filter and AD conversion circuit based on AD7608are designed.Signal processing analysis implements the function of vibration signal denoising and fault modal analysis. The thesis adopts wavelet denoising method to remove noise in vibration signal. Hilbert-Huang Transform is an adaptive time-frequency analysis method based on the signal itself. Aiming at the characteristic of vibration signal, an axlebox bearing fault diagnosis method based on Hilbert-Huang Transform is proposed. The Hilbert spectrum and Hilbert marginal spectrum with obvious fault features could be obtained by Hilbert-Huang Transform. The results show that the fault diagnosis method based on Hilbert-Huang Transform can effectively identify the fault.Fault feature extraction implements the function of eigenvectors extraction which could reflect the fault of axlebox bearing. This thesis extracts multi-feature parameters including time domain parameters, energy moments of IMF and fault characteristic amplitude ratio, which could reflect the health status of the axlebox bearing entirely.Fault classifier based on BP Neural Network is designed for fault pattern recognition to realize the nonlinear mapping between multi-feature parameters and fault state. To classify different conditions of axlebox bearing, the diagnostic accuracy is above90%.