Research on the selection of wavelet function for the feature extraction of shock fault in the bearing diagnosis

For the rolling bearing diagnosis, how to identify the fault feature effectively is the key issue. Due to the resonance modulation characteristic induced by shock fault of the rolling bearings, the wavelet transform technology can extract the modulation information effectively. On the other hand, as there are no fixed kernel functions in wavelet analysis, the transform results are closely related to the wavelet base function types. According to shock and modulation characteristic of localized fault, how to select the proper wavelet base function is discussed in this article. Through analyzing the simulation signal of outer race fault, the base functions for the discrete wavelet transform are optimized. The results have shown that dmey wavelet mother function is prior to other wavelet functions in the shock fault feature extraction. Furthermore, demodulation technology based on Hilbert transform is used to analyze the detailed wavelet decomposition coefficient which contains the modulation phenomenon. And the fault feature can be identified obviously. Finally, the vibration signal collected from fault bearing in the wire rolling mill is decomposed using optimized dmey wavelet. The further FFT analysis on low frequency wavelet decomposition coefficient can also identify the incipient fault feature successfully.