A fault diagnosis model for inter-shaft bearing based on attention mechanism fusion and deep domain adaptation

Fault diagnosis of inter-shaft bearing is crucial for enhancing the reliability and safety of aero-engines. However, complex and variable working conditions, along with noise interference, make it challenging to ensure effective data collection and accurate fault diagnosis. To address the issue of inconsistent data distribution caused by varying working conditions, this paper proposed a deep transfer learning-based inter-shaft bearing fault diagnosis model called EPBSP. The model first used Continuous Wavelet Transform (CWT) to convert vibration signals from source and target conditions into time-frequency images. Then, a ResNet50-based feature extractor was constructed, incorporating a fusion attention mechanism residual block to effectively capture local and global information of fault features. Simultaneously, a joint loss function was constructed by combining Batch Spectral Penalization (BSP) regularization method with Domain-Adversarial Neural Network (DANN) for adversarial training, reducing the feature distribution difference between source and target domains and improving the model’s domain adaptation capability. Finally, the proposed method was analyzed using datasets from Harbin Institute of Technology and a self-built test rig, achieving fault diagnosis accuracies of 99.19% and 99%, respectively. The method outperformed existing fault diagnosis models in terms of both diagnostic accuracy and generalization ability.