Fault Diagnosis Method for Rail Transit Train Axle Box Bearing Based on the Fusion of Vibration Acceleration and Acoustic Signal

Abstract

Objective Axle box bearing is a key component of rail transit train bogie, and its health state directly affects the train operation safety. Therefore, it is necessary to establish a more scientific and efficient fault diagnosis method for axle box bearing to effectively extract bearing fault characteristic information under strong noise interference. Method With the vibration acceleration and acoustic signal (abbreviated as vibration-acoustic signal) as the research object, the fault characteristics of the axle box bearing are analyzed, and an optimal signal noise reduction method with bandpass convolution filtering is proposed. In this method, the original signal is divided into several frequency bands in the frequency domain, different bandpass filtering parameters of each frequency band are determined and a multi-channel bandpass convolution filter bank is constructed. The optimal filtered signal is selected by using time-domain index segmental kurtosis, and demodulated by weighted frequency energy operator to identify the bearing fault spot. Result & Conclusion With the proposed method, the fault characteristics of the vibration-acoustic signal can be extracted under strong interference noise. Both the simulation and on-site test results verify the validity of the method. The fault diagnosis conclusions of the vibration-acoustic signal can be mutually verified, further improving the accuracy of the axle box bearing fault diagnosis.

Keywords

• rail transit train
• axle box bearing
• fault diagnosis method
• vibration acceleration signal
• acoustic signal
• bandpass filtering
• weighted energy operator