The effect of fast kurtogram to extract rolling bearings’ weak fault signals is not obvious under interference of strong background noise. Here, rolling bearings’ weak fault feature extraction was conducted with the combination of iterative filtering and fast kurtogram. The faulty bearing’s vibration signals were adaptively decomposed into a group of intrinsic mode components with iterative filtering. The iterative filtering method was used to denoise rolling bearing’s vibration signals with strong noise. An optimal band-pass filter was constructed using the fast kurtogram．Finally, the envelope spectra of the filtered signals were compared with fault feature frequencies of rolling bearings to judge the diagnosed bearing’s fault types．The effectiveness and advantages of the proposed method were verified with numerical simulation and tests.
ZHONG Xianyou，TIAN Hongliang，ZHAO Chunhua，CHEN Baojia，CHEN Fafa.
Fault feature extraction for rolling bearings’ weak faults based on iterative filtering and fast kurtogram[J]. Journal of Vibration and Shock, 2018, 37(9): 190-195
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