# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Construction of Multi-Local Fault Diagnosis Model Based on Bi-LSTM
#### 3.1.3 Local Fault Diagnosis Model Based on Bi-LSTM
In addition, the Bi-LSTM network has the ability of adaptive feature extraction and the ability to process high-dimensional nonlinear data so that it can directly use the time-domain vibration data and time-domain current data to make the end-to-end intelligent diagnosis of bearing operating status. Therefore, we use the Bi-LSTM network to perform adaptive feature extraction on the bearing time-domain vibration data and current data. In the proposed method, after preliminary cleaning of the bearing time-domain vibration data and current data through the noise reduction autoencoder, the obtained reconstructed current sensor data and reconstructed vibration sensor data are input into the Bi-LSTM network in parallel for forward and reverse bidirectional operations, deeply mine the feature information of the signal in each fault state and deepen the level of time series feature extraction. The hidden layer features obtained by Bi-LSTM layer conversion are:  
\[D=(d_{1},d_{2},d_{3},\dots,d_{n}). \tag{15}\]  
Figure 4.0 The structure diagram of the LSTM memory unit. Due to the long-term operation of industrial motor bearings in a complex environment and variable working conditions, the original monitoring data characteristics of the bearings in different periods have obvious time-varying differences, which seriously affects the feature extraction efficiency of motor bearing signals using the Bi-LSTM network for industrial applications. To improve the problem that the Bi-LSTM network is susceptible to insensitive features and maximize the use of the extracted relevant features, we introduce an attention mechanism based on the Bi-LSTM network. Features are screened and optimized to highlight the contribution and importance of key features to the results of fault identification and classification by using it. In this way, more robust deep features can be obtained, and the training efficiency of the neural network can be improved to a certain extent, laying a good foundation for realizing high precision bearing fault diagnosis. The specific construction process of the attention mechanism for adaptive dynamic feature weighted fusion can be expressed as follows:  
Figure 4.1  
Figure 4.2  
Figure 4.3  
where \(R\) is the attention scoring function, \(\lambda_{i}\) represents the attention weight coefficient, \(F_{A}\) represents the activation function, \(m\) is the feature expression obtained after weighted fusion, \(d_{i}\) represents the hidden layer state corresponding to the input sequence, \(e\) represents the bias, and \(W_{s}\) represents the variable weight matrix. Then, the fully connected layer accepts the feature vector \(m\) output by the attention mechanism layer. Its forward propagation formula is as follows:  
Figure 4.4  
where \(T\) is the output of the fully connected layer, \(p\) is the activation function of the fully connected layer, and \(m\) is the output of the attention mechanism layer. Finally, as shown in the equation below, the fully connected layer realizes the feature space transformation through weighted calculation and then inputs the classification layer to realize the output \(U\) of the predicted target category. Figure 4.5