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

Highlights：

• 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.