The Doppler Effect based acoustic source separation for a wayside train bearing monitoring system

Abstract Wayside acoustic condition monitoring and fault diagnosis for train bearings depend on acquired acoustic signals, which consist of mixed signals from different train bearings with obvious Doppler distortion as well as background noises. This study proposes a novel scheme to overcome the difficulties, especially the multi-source problem in wayside acoustic diagnosis system. In the method, a time–frequency data fusion (TFDF) strategy is applied to weaken the Heisenberg׳s uncertainty limit for a signal׳s time–frequency distribution (TFD) of high resolution. Due to the Doppler Effect, the signals from different bearings have different time centers even with the same frequency. A Doppler feature matching search (DFMS) algorithm is then put forward to locate the time centers of different bearings in the TFD spectrogram. With the determined time centers, time–frequency filters (TFF) are designed with thresholds to separate the acoustic signals in the time–frequency domain. Then the inverse STFT (ISTFT) is taken and the signals are recovered and filtered aiming at each sound source. Subsequently, a dynamical resampling method is utilized to remove the Doppler Effect. Finally, accurate diagnosis for train bearing faults can be achieved by applying conventional spectrum analysis techniques to the resampled data. The performance of the proposed method is verified by both simulated and experimental cases. It shows that it is effective to detect and diagnose multiple defective bearings even though they produce multi-source acoustic signals.