In order to improve the maintenance efficiency of the motor and realize the real-time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high-frequency signal is denoised to improve the signal-to-noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.

中文翻译：

---

基于小波和注意力机制的电机故障诊断算法

为了提高电机的维修效率，实现电机的实时故障诊断功能，提出了一种基于小波和注意力机制的电机故障诊断算法。首先通过小波变换对电机振动信号进行分解，对高频信号进行去噪，提高信噪比。其次，将小波分解后的频带和时间维度作为输入数据，利用卷积神经网络融合数据的频带特征，利用双向门控循环单元融合时间序列特征。然后，利用注意力机制自适应地整合不同时间点的特征。最后通过分类器识别实现电机故障诊断和预测。实验结果表明，