## 4 Case Study and Verification
### Fault Diagnosis of Bearings in the Roller Table System of the Annealing Kilin  
Here, the proposed method is introduced to diagnose the health condition of the roller and through shaft bearings in the roller table system of the annealing kilin. In a glass production plant, vibration signals of the annealing kilin roller system were collected, as shown in Figure 15. The collected bearing vibration signals were divided into two states: normal and fault. The sampling frequency was 12 KHz, and vibration signals of the roller bearing system were collected using a three-axis acceleration sensor and an NI 9234 data acquisition card. The collected vibration signals under the normal and fault conditions of the bearings are shown in Figure 16. According to the proposed method, the original signal was decomposed, and the effective component was selected to filter the noise interference. Then, the SNR of the roller bearing vibration signal was 23.11, and the mean square error was 0.52. The SNR of the through shaft bearing vibration signal was 24.63, and the mean square error was 0.96. According to the CWT method, the time-frequency representations are shown in Figure 17, and on the basis of the histogram equalization method, the local features of the enhanced time-frequency map are clear and prominent. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Experiment Method** & **Average Accuracy/\%** & **Error/\%** & **Time/min** \\ \hline Experiment 1 & CNN-LSTM & 85.67 & 3.53 & 86.15 \\ Experiment 2 & DCORAL & 90.52 & 3.54 & 82.46 \\ Experiment 3 & DDA & 94.23 & 1.95 & 92.65 \\ Experiment 4 & The proposed method & 99.11 & 0.86 & 97.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental average diagnostic results. Figure 14: Accuracy rate of fault transfer diagnosis of rolling bearing. Figure 16: Vibration signals of the roller table system of the annealing kiln. (**a**) Vibration signal of normal roller table bearing. (**b**) Vibration signal of fault roller table bearing. (**c**) Vibration signal of normal through shaft bearing. (**d**) Vibration signal of fault through shaft bearing. (**e**) Denoised vibration signal of normal roller table bearing. (**f**) Denoised vibration signal of fault roller table bearing. (**g**) Denoised vibration signal of normal through shaft bearing. (**h**) Denoised vibration signal of fault through shaft bearing. Figure 15: Healthy condition monitoring for the roller system of the annealing kiln. (**a**) Vibration signal collection of the roller table bearing (In the red box) (**b**). Vibration signal acquisition of the through shaft bearing (In the red box). According to the proposed method, the roller table bearing datasets were organized into 400 labeled samples and divided into the training and testing sets in a 9:1 ratio. The datasets were inputted into the roller bearing intelligent diagnosis network and trained 10 times. The average accuracy of the model reached 99.65%, and the loss function and accuracy curve of roller table dataset is shown in Figure 18. After 500 training iterations, the fluctuation of the accuracy and loss curve converged, and the network was completely trained. Using the roller table bearing datasets as the source domain and the through-shaft bearing dataset as the target domain, the roller table bearing data and 80 unlabeled through shaft bearing data were used as the training set, and 40 labeled through shaft bearing data were used as the test set. The intelligent transfer diagnosis network for through-axis bearings was introduced and trained 10 times. The average accuracy of the model for the through shaft bearing was 99.20%. The training curve is shown in Figure 19, which shows that the accuracy and loss curve fluctuates steadily and ultimately converges. This curve indicates that the proposed diagnostic model is effective and feasible and has high a recognition accuracy.