Rolling bearing fault feature extraction based on maximum correlated kurtosis deconvolution and improved autocorrelation spectral kurtograph

Abstract

In order to further improve the separation and detection accuracy of bearing fault characteristics, A new method for early fault diagnosis of rolling bearings based on Maximum Correlated Kurtosis Deconvolution and autocorrelation kurtograph was proposed. Firstly, the vibration signal of bearing fault is denoised by Maximum Correlated Kurtosis Deconvolution; Then, the improved autocorrelation spectral kurtograph is used to select the optimal frequency center and bandwidth of fault features. According to the optimal frequency center and bandwidth, the band pass filtering is carried out to remove noise and random pulse irrelevant components in the band signal. Finally, the sub-signal after bandpass filtering is analyzed by envelope spectrum, identify fault frequency and realize early fault diagnosis of rolling bearing. In the experiment, different types of bearing fault data verify the effectiveness of the proposed method.