Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data

Intelligent fault diagnosis methods are significant to mitigate the dependency on expert knowledge and the cost. For the limited faulty data and variational working conditions of real scenarios, cross-domain diagnosis using existing diagnosis models is widely discussed. Especially, methods based on cross-domain transfer learning show great potentiality. However, the class imbalanced data of actual working conditions make it difficult to learn the actual fault feature distribution. To this end, a transfer residual network augmented with explicit weight self-assignment strategy based on meta data(TRN-EWM) is proposed. Specifically, we use a domain-shared ResNet to extract depth features of the data, which effectively avoid gradient disappearance and improve classification performance. Then, to lessen diagnosis difficulties in cross-domain and fully mine the actual feature distribution of the samples, a class imbalanced cross-domain transfer method is carried out. Ultimately, we creatively construct an explicit weight self-assignment strategy based on meta data for sample weight rebalancing, which prevents the dominance of major classes and the overfitting of minor classes. Two transfer experiments are conducted, and average cross-domain diagnosis accuracy of 99.60% is achieved by the proposed method, which shows the effectiveness in bearing fault diagnosis. • A novel cross-domain transfer fault diagnosis method for class imbalanced samples is proposed. • Fault feature extractor based on deep residual network is constructed to avoid gradient disappearance and improve the diagnosis performance. • Cross-domain transfer is carried out to reduce the degree of difficulty in diagnosis. • Explicit weight self-assignment strategy based on meta data is adopted to optimize the sample weighting process with class imbalance.