## 4 Experiments
### Experiments and Evaluation Method

Table 3: The possible state of each device in A-class insulation panel production line (Original dataset). Figure 6: Original data captured from various sensors. Subplots (**a,b**) are curves of voltage and current, in which abnormal data exist. Training. [PERSON] was selected as the optimizer with a momentum of \(\beta_{1}=0.95\), \(\beta_{2}=0.997\) in the training process. Batch size was fixed to 128, and the learning rate was 0.0002 at the beginning. The decay ratio of learning rate was 0.98 per five epochs; hence, the final learning rate was \(2e^{-4}\times 0.98^{40}\approx 8.9e^{-5}\). In the training process, the changes of mean square error (MSE) loss of the training and testing set are shown in Figure 8. In the first 30 epochs of training, the mean square error loss of the training set and test set showed a rapid decreasing trend, but after epoch 160, the decreasing trend became slower. As can be seen from Figure 8, in the training and testing set, MSE loss after training of epoch 180 are both minimum values. To select the model with the best performance, the model parameters after epoch 190 of training were selected as the final CNN-LSTM model parameters. At this time, the MSE loss of the training and testing set were about 0.4227 and 0.4213, respectively. Figure 8: MSE loss curves of the proposed CNN-LSTM model. (**a**) Loss curve of the training dataset. (**b**) Loss curve of the testing dataset. Figure 7: The preprocessed dataset of multi-sensor signals. Subplots (**a–f**) are signals of voltage, current, rotation, vibration-x, vibration-y and pressure, respectively. Subplots (**a,b**) refer to the starting moment, and subplots (**c–f**) reflect the normal running state of corresponding equipment. Testing. To verify the feasibility and effectiveness of the proposed CNN-LSTM model for the fault diagnosis method of an A-class thermal insulation board production line, four evaluation indexes, i.e., accuracy, precision, recall and F1-sore, were adopted to evaluate the prediction of the model and compare these with other algorithms, e.g., RNN [39], LSTM [40], SVM [33] and 1D-CNN [41]. In the same dataset, the proposed LSTM fault diagnosis model was used to train the multi-sensor dataset of an A-class insulation panel production line. The comparison is shown in Table 4, and the formulations can be described as follows:  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{5}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{6}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{7}\]  
\[\text{F}_{1}=2\cdot(\text{Precision}\cdot\text{Recall})/(\text{Precision}+ \text{Recall}) \tag{8}\]  
where, TP, FP, TN and FN denote True Positive, False Positive, True Negative and False Negative. All algorithms involved in the comparison were slightly modified to fit the task, which was to predict the state of the production line in the next 10 s by the data of the last 100 s. Time consumption refers to the cost of time per prediction. The confusion matrix is shown in Figure 9 to provide further insights. The essential problem is how to predict if the production line is running properly or not. Although the output of the proposed CNN-LSTM model is multi-classification, false classifications among abnormal states of different equipment are not concerned. In other words, the multi-classification can be regarded as binary classification. Therefore, the left multi-classification confusion matrix in Figure 9 can be converted to a right matrix, which simplifies the fault diagnosis problem, in which 0 and 1 represent the normal and abnormal states, respectively. In terms of fault diagnosis performance, the proposed CNN-LSTM model was obviously superior to other network based on RNN and SVM-based methods. Compared with typical deep RNN model and LSTM models applied in recent years, the proposed