Fault Diagnosis of Axle Box Bearing with Acoustic Signal Based on Chirplet Transform and Support Vector Machine

Abstract

Acoustic fault diagnosis technology equipment is non-contact, and the acoustic signal is easy to access. However, it is difficult to extract the feature information of the acoustic signal with low signal-to-noise ratio (SNR). In this paper, a fault diagnosis model (FDM) of axle box bearing based on Chirplet transform (CT) and support vector machine (SVM) is established to diagnose bearing fault based on acoustic signal. The availability of the model is verified by comparing with the vibration acceleration signal bearing fault diagnosis results, and the correctness of the model is verified by utilizing the open database of Western Reserve University. The acoustic-vibration comprehensive bearing fault diagnosis experiment platform (AVEP) is established to investigate the acoustic signal and acceleration signal diagnosis accuracy. The results suggest that, based on the FDM, the diagnosis accuracy and stability of acoustic signal are not as good as acceleration signal when the number of samples is small under the single condition; the diagnosis accuracy of acoustic signal is similar to that of acceleration signal when the number of samples is enough under the multiple condition, which provides a reference for the application of acoustic fault diagnosis technology in engineering in the future.