Bearing fault detection based on optimal wavelet filter and sparse code shrinkage

The presence of periodical impulses in vibration signals often indicates the occurrence of machine faults. However, it is difficult to detect the impulses at the early stage of fault because they are rather weak and often overwhelmed by heavy noise and higher-level macro-structural vibrations. Therefore, a proper signal processing method is needed. In this paper, to extract the impulsive features buried in the vibration signal, a hybrid method which combines Morlet wavelet filter and sparse code shrinkage (SCS) is proposed. First, the wavelet filter is optimized by differential evolution (DE) to eliminate the interferential vibrations and obtain the fault characteristic signal. Then, to further enhance the impulsive features and suppress residual noise, SCS which is a soft-thresholding method based on maximum likelihood estimation (MLE) is applied to the filtered signal. The results of simulated experiments and real bearing vibration signal analyses verify the effectiveness of the proposed method in extracting impulsive features from noisy signal.