针对Transformer在航空发动机故障诊断中存在空间信息特征建模能力不足、计算复杂度较高的问题，提出一种基于路径聚合网络的Swin Transformer的故障诊断方法。该方法将路径聚合网络嵌入到Swin Transformer网络中，提高模型多尺度融合特征金字塔顶层信息和底层信息的效率，并采用窗口多头自注意力模块和移动窗口多头自注意力模块，有效降低提取空间信息特征的计算复杂度，并促进信息的流动和特征的传递。最后，将提出的方法应用到航空发动机滚动轴承故障诊断中。实验结果表明，提出的方法明显优于Transformer和传统Swin Transformer方法，在保证识别精度的同时，提高了模型的识别速度。

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 by fusing the multiscale feature pyramid from top and bottom information. Then, window-based multi-head self-attention and shift window-based multi-head self-attention modules are 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 is applied in fault diagnosis for 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 and improving the recognition speed of the model.