Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN-AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.

中文翻译：

---

基于MCNN和GRU网络的注意机制的滚动轴承早期故障检测方法。

针对滚动轴承的早期故障诊断问题，提出了一种基于多尺度卷积神经网络和带注意机制的门控递归单元网络（MCNN-AGRU）的滚动轴承早期故障检测方法。该方法首先将滚动轴承振动信号的多个时间尺度输入到卷积神经网络中，以通过多尺度数据处理来训练模型，然后添加带有注意机制的门控递归单元网络以使模型具有预测性。最后，将实际值与预测值之间的重构误差用于检测早期故障。这种方法的训练数据只是普通数据。有效解决了滚动轴承运行状态监测和性能下降评估中的早期故障检测。它使用多尺度数据处理方法来使CNN提取的特征更加健壮，并使用具有注意机制的GRU网络来使该方法的预测能力不受数据长度的影响。实验结果表明，本文提出的MCNN-AGRU滚动轴承早期故障诊断方法可以有效地检测出滚动轴承的早期故障，并可以有效地识别出滚动轴承故障的类型。