Fault diagnosis method based on Swin Transformer with path aggregation networks

To address the insufficient spatial information feature modeling capability and high computational complexity of the Transformer in aero-engine fault diagnosis, a fault diagnosis approach was proposed based on the Swin Transformer with path aggregation networks ( PANet ). In the proposed method, the Swin Transformer with PANet improves the efficiency of fusing the multi scale feature pyramid top and bottom informations. Then, window-based multi-head self-attention and shift window-based multi-head self-attention modules were used to reduce the computational complexity in spatial information feature extraction. Therefore, the information flow and feature transmission can be promoted effectively. Finally, the proposed method was applied in fault diagnosis of the aero-engine rolling bearings. The experimental results show that the proposed method is better than the Transformer and traditional Swin Transformer methods. While guaranteeing the recognition accuracy, the recognition speed of the model is improved. © 2024 Chinese Vibration Engineering Society. All rights reserved.