Cross-Domain Diagnostic Method of Rolling Bearings Based on Refined Composite Zoom Multiscale Weighted Permutation Entropy-Ensemble Feature Transfer Learning

In response to the issues of poor noise resistance, difficulty in extracting faint features, and confusion of signal characteristics across different fault statuses observed in existing entropy features for cross-domain diagnosis of rolling bearings, a refined composite zoom multiscale weighted permutation entropy (RCZMWPE) index has been proposed. This index is capable of extracting faint fault signals across the entire frequency band. Furthermore, a diagnostic model based on RCZMWPE, incorporating ensemble feature learning and semisupervised manifold feature transfer for cross-domain fault diagnosis, has been constructed. First, by decomposing the raw vibration signals, effective components are selected for reconstruction and enhancement to reinforce the expression of sensitive fault features. Second, an ensemble feature learning classification strategy based on multikernel twin underlying classifiers is proposed, which continuously enhances the learning capability of the weak classifiers for common features and integrates them into a strong classifier. Then, the dynamic distribution differences of cross-domain feature samples are measured, and the manifold feature transfer strategy is utilized to optimally map and identify the health status of bearings. Finally, the effectiveness and robustness of the proposed method are demonstrated through four public datasets of different test benches, four sets of real engineering case data from different equipment, and aeroengine bearing data with different degrees of fault damage. Additionally, a comparison with seven published entropy features and six fault diagnosis methods from references demonstrates that the proposed method exhibits superior performance in handling multisource cross-domain diagnostic tasks across different equipment and operating conditions.