## 3 Bearing Fault Recognition Based on SSL-VCST Method
### Experiment 1: Constant Speed Conditions of Rotating Machinery  
Some scholars pointed out that among all bearing faults, outer ring failures and inner ring failures account for about 90%, and rolling element failures account for about 10% [38]. According to the above statistical data, the CWRU datasets are used for experimental setup, as shown in Table 4. First, CWT time-frequency images of all samples under four different states must be prepared. In actual operating conditions, the total number of labeled samples is relatively small, among which normal samples are the most abundant and easy to obtain. Therefore, the training sets include labeled samples and unlabeled samples, in which 100 normal samples are used for each of the three tasks, respectively. The samples for the other three fault states contain three different damage levels, and the sample quantity for each level is the same. In unlabeled samples, each state contains 100 samples, and the proportion of the three different damage levels is basically balanced. The testing sets are also set in the way, but the sample size is 50.  
First, fault diagnosis is performed using CNN on the unbalanced sample sets as a basis for comparison. Then, after sample supplementation using the CVAE-SKEGAN method proposed in Section 2, a balance of 1:1:1:1 is achieved between normal and three fault samples. Fault diagnosis is carried out using SSL-VCST, SSL-CNN, and SSL-Swin-T to compare the fault diagnosis accuracy. SSL-CNN and SSL-Swin-T involve adding SSL mechanisms to both the CNN network and Swin-T network, respectively. The batch training size, learning rate, and optimization method are the same for all four models.  
It can be known from Figure 8 that sample imbalance has a great impact on fault classification, and the more imbalanced the ratio between fault samples and normal samples, the worse the performance of fault classification. In the case of imbalanced data, using data generation for sample supplementation can improve classification accuracy. After balancing, the performance of the SSL-VCST model is clearly better than the other three methods. It can be proved that under constant rotational speed conditions of rotating machinery, the CVAE-SKEGAN method proposed for sample supplementation and the SSL-VCST model for fault diagnosis can better solve the problems of sample imbalance and labeled sample shortage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **Sample Type** & **Normal** & \begin{tabular}{c} **Inner Race** \\ **Fault** \\ \end{tabular} & \begin{tabular}{c} **Outer Race** \\ **Fault** \\ \end{tabular} & \begin{tabular}{c} **Ball Fault** \\ \end{tabular} \\ \hline T1 & Training sets: & 100 & 27 & 27 & 6 \\ T2 & Labeled samples & 100 & 45 & 45 & 10 \\ T3 & \begin{tabular}{c} Training sets: \\ Unlabeled samples \\ \end{tabular} & 100 & 90 & 90 & 20 \\ \hline T1-T3 & \
\begin{tabular}{c} Training sets: \\ Unlabeled samples \\ Testing sets \\ \end{tabular} & 100 & 100 & 100 & 100 \\ T1-T3 &
\begin{tabular}{c} Testing sets \\ \end{tabular} & 50 & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental dataset setup under constant speed conditions.