针对传统故障诊断模型对特征提取不全面，单一模型稳定性和泛化性差的问题，提出了一种基于多头自注意力机制的多尺度卷积神经网络和双向门控循环单元模型，从空间和时序层面实现特征提取。该模型采用原始一维振动信号作为输入，使用不同尺寸卷积核的卷积网络捕获多尺度信息。引入多头自注意力机制，根据输入的不同部分动态调整输出权值，忽略冗杂信息并对所提取特征进行加权融合，将融合后的特征输入至BiGRU（bidirectional gated recurrent units）网络，通过双向信息融合机制，对来自过去和未来两个方向的信息进行挖掘，捕捉输入序列不同部分间的依赖关系。最后，通过Softmax分类实现轴承故障诊断。在3种轴承数据集上进行实验验证，结果表明，所提模型性能指标表现优异，具有良好的泛化性和可行性。

To address the issues of incomplete feature extraction, poor stability, and limited generalization in traditional fault diagnosis models, a model based on a multi-scale convolutional neural networks (MCNN), bidirectional gated recurrent units (BiGRU), and multi-head self-attention mechanism (MSA) was proposed. The model was designed to achieve comprehensive feature extraction from both spatial and temporal perspectives. It took raw vibration signals as input, and multi-scale features were extracted through convolution kernels of different sizes. A multi-head self-attention mechanism was used to dynamically adjust output weights, disregarding redundant information and weighting the extracted features for fusion. Then the fused features were input into a BiGRU network, which utilized a bidirectional information fusion mechanism to explore information from both past and future directions, capturing dependencies between different parts of the input sequence. Finally, Softmax was employed for classification. Experimental validation was conducted using three bearing fault datasets, and the results show that the proposed model has excellent performance metrics on different datasets and showcases good generalization and feasibility.