Acoustic Diagnosis of Rolling Bearings Fault of CR400 EMU Traction Motor Based on XWT and GoogleNet

Acoustic diagnosis has been a research hotspot in recent years because of the advantages of noncontact signal acquisition. However, acoustic diagnosis technology has not been applied to bearing fault diagnosis of Electric Multiple Units (EMU) traction motor. Traditional fault diagnosis methods are difficult to diagnose acoustic signals with complex noise. An intelligent fault diagnosis method based on Cross Wavelet Transform (XWT) and GoogleNet model is proposed in this paper. Firstly, the fault feature enhancement algorithm is proposed using XWT and bandpass filtering. Secondly, the CR400 EMU traction motor bearing fault test bed is built to collect real fault acoustic signals from two different positions, then XWT is applied to the original signal to identify the fault feature frequency band, then bandpass filtering is used to filter out the noise frequency band other than the fault feature frequency band. Finally, the kurtosis spectrum of the denoised signal and the original signal are input into GoogleNet, respectively, for fault classification. The result shows that (1) GoogleNet achieves 98.23% accuracy in the fault classification for denoised signals, while only 89.66% accuracy for the original signals. (2) Deep learning is an effective method for the acoustic diagnosis of motor bearing faults in EMU trains.

Automated summary

This paper proposes an intelligent fault diagnosis method based on Cross Wavelet Transform (XWT) and GoogleNet model for acoustic diagnosis of motor bearing faults in Electric Multiple Units (EMU) trains. The fault features are enhanced using signal processing and filtering, and deep learning is used for fault classification. Experimental results show that the method achieves high accuracy.