Rolling Bearing Fault Diagnosis Based on Wavelet Overlapping Group Shrinkage and Extended Envelope Hierarchical Multiscale-Weighted Permutation Entropy

Rolling bearing vibration signals contain rich fault feature information. However, their periodic pulse feature is often interfered with by strong background noise, which reduces the feature recognition ability of fault diagnosis strategies. Therefore, accurately extracting periodic pulse information under strong background noise is a key challenge in rolling bearing fault diagnosis. To address this, a fault feature extraction strategy combining wavelet overlapping group shrinkage (WOGS) and extended enveloped hierarchical multiscale-weighted permutation entropy (EEHMWPE) is proposed. First, wavelet decomposition is applied to decompose original vibration signals into wavelet coefficients, with WOGS adaptively adjusting the shrinkage level based on energy relationships to effectively suppress noise. Next, for the denoised signal, EEHMWPE extracts periodic pulse features by integrating envelope analysis, weighting, and extended statistical features. Envelope processing enhances fault-induced impulses, the weighting scheme highlights dominant fault patterns, and extended statistical features further improve the class separability between normal and fault signals. Finally, the strategy was validated on the bearing test bench, CWRU, and HUST datasets, all of which achieved over 99% accuracy with superior feature recognition.