The phenomenon of Doppler shift in the acoustic signal acquired by a microphone amounted beside the railway leads to the difficulty for fault diagnosis of train bearings with a high moving speed. To enhance the condition monitoring performance of the bearings on a passing train using stationary microphones, the elimination of the Doppler shift should be implemented firstly to correct the severe frequency-domain distortion of the acoustic signal recorded in these conditions. In this paper, a Doppler shift removal method is proposed based on instantaneous frequency (IF) estimation (IFE) for analyzing acoustic signals from train bearings with a high speed. Specifically, the IFE based on short-time Fourier transform is firstly applied to attain the IF vector. According to the acoustic theory of Morse, the data fitting is then carried out to achieve the fitting IFs with which the resampling sequence can be established as the resampling vector in time domain. The resampled signal can be finally reconstructed to realize fault diagnosis of train bearings. To demonstrate the effectiveness of this method, two simulations and an experiment with practical acoustic signals of train bearings with a crack on the outer raceway and the inner raceway have been carried out, and the comparison results have been presented in this paper.
Rolling bearing fault diagnosis via STFT and improved instantaneous frequency estimation method
