针对传统的故障诊断方法使用单一模态的特征提取时，存在故障信息丢失、故障状态表征局限性和故障识别率不高等问题，提出了一种基于优化双向门控循环单元（bidirectional gatedrecurrent unit,BiGRU）和Swin Transformer的发动机齿轮传动机构故障诊断方法。首先，利用格拉姆角差场（gramian angular difference field,GADF）将采集的一维振动信号转化为具有时频特征的二维图像；其次，构建了双通道特征提取网络：通道一采用Swin Transformer网络挖掘信号的时频图像特征，通道二则利用引入注意力机制的改进BiGRU-ATT网络捕捉信号的深层时序特征；然后，将时频图像特征和时序特征进行特征融合；最后，对融合后的特征利用Softmax分类器完成不同故障的分类识别。为验证所提方法的有效性，利用动力传动故障诊断综合试验台的数据集进行试验验证，并与其他智能诊断方法进行对比试验。结果表明：所提方法故障识别率最高，平均诊断准确率为99.68%，具有可行性，对发动机齿轮传动机构的智能诊断和实际应用有一定的指导意义。

Traditional fault diagnosis methods relying on single-modality feature extraction suffer from issues such as loss of fault information, limited representation of fault states and low fault recognition rates. Fault diagnosis method of engine gear transmission was hence proposed based on an optimized bidirectional gated recurrent unit (BiGRU) and Swin Transform. The Gramian Angular Difference Field (GADF) was first employed to transform the acquired one-dimensional vibration signal into a two-dimensional image with time-frequency features. Then a dual-channel feature extraction network was designed, Channel 1 employed a Swin Transformer network to extract time-frequency image features from the signal, while Channel 2 utilized an improved BiGRU-ATT network with an attention mechanism to capture deep temporal features of the signal. Subsequently, the feature fusion between time-frequency image features and temporal features was conducted. Softmax classifier was finally applied to the fused features for fault classification. To validate the proposed method, experiments were conducted using a dataset from a comprehensive powertrain fault diagnosis test bench. Comparative experiments with other intelligent diagnostic methods demonstrated that the proposed approach achieved the highest fault recognition rate with an average diagnostic accuracy of 99.68%. This confirmed its feasibility and provided valuable insights for the intelligent diagnosis and practical application of engine gear transmission mechanisms.