Trackside acoustic detection of axle bearing fault using wavelet domain moving beamforming method

Trackside acoustic detection is a promising way for fault diagnosis of train axle bearings. There are two main tough issues in the trackside acoustic detection, which are the signal distortion due to the Doppler effect and strong noise interference from other subsystems of the vehicles. This study presents a solution to overcome both issues for axle bearing fault diagnosis by using a Wavelet domain Moving Beamforming (WMB) method. Firstly, the time domain acoustic signal acquired by a microphone array is transformed to the wavelet domain, and the Doppler effect is removed through a proposed wavelet domain resampling method. Then, the wavelet domain beamforming is applied to enhance the signal of axle bearing and reduce the interference noise. Afterwards, according to the kurtoses of frequency components, the components with smaller kurtoses are discarded to further enhance the impulsive component carrying the fault information. The train axle bearing faults can finally be diagnosed by observing the envelope spectrum of the processed signal. The results of numerical simulations and experiments verify the feasibility of the WMB. The influence of measurement setup parameters is also investigated and discussed.