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. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a transfer residual network augmented with explicit weight self-assignment strategy based on meta data (TRN-EWM) is proposed for cross-domain fault diagnosis. By extracting depth features and conducting class imbalanced cross-domain transfer, this method effectively addresses the diagnosis difficulties in actual working conditions and achieves high diagnosis accuracy.