Weak fault feature extraction method of rolling bearing based on wavelet packet decomposition and optimized MCKD

Abstract

The early fault characteristics of rolling bearings are weak, especially in a strong noise environment, which are more difficult to extract; therefore, a method based on wavelet packet decomposition, multi-verse optimizer, and maximum correlated kurtosis deconvolution for weak fault feature extraction of rolling bearings is proposed. First, the original vibration signal is decomposed using wavelet packet decomposition, followed by proposing a signal reconstruction method combining the Pearson correlation coefficient and energy ratio to effectively remove noise from the original signal. Second, the parameters L and M of Maximum Correlated Kurtosis Deconvolution (MCKD) are optimized using the multi-verse optimizer algorithm to obtain optimal filter settings. Subsequently, the enhanced reconstruction signal fault features are obtained using the optimized MCKD algorithm. Finally, signal fault features are extracted through envelope spectrum analysis, ultimately achieving the extraction of weak fault features in rolling bearings. The simulation and experimental analysis results demonstrate that the wavelet packet decomposition-MMCKD (Multiverse Optimization Algorithm for Maximum Correlated Kurtosis Deconvolution) feature extraction method not only removes noise from the vibration signal of rolling bearings but also enhances weak fault features, enabling the early extraction of subtle fault features in rolling bearings.