## 3 Bearing Fault Recognition Based on SSL-VCST Method
### Assessment of Classification Ability with Insufficient Labeled Samples  
Too inadequate of training samples can lead to limited learning of data distribution by deep learning models. Therefore, different sizes of training datasets are set to compare applicability and classification accuracy of Swin-T, VCST, and SSL-VCST networks based on a small sample. In all the classification experiments in this article, the training samples include real samples and generated samples. All real samples in the test sets and train sets come from different time periods of vibration signals based on the same batch of bearing and there is no overlap between test samples and train samples. All the original experimental data are labeled, and during the operation, a portion of the data is selected as labeled data to train the network, while a portion of the data is manually unlabeled to form unlabeled data. Considering the actual working environment where there is limited labeled data, GAN-generated samples are used to expand the labeled data. The experiments use the CWRU dataset with 10 types of data, and 4 sets of experiments are designed with labeled samples and unlabeled samples. Test sets evenly sampled from the 10 types of data, with the same-sized samples for each type of data. The total sample capacity of each task is shown in Table 3. Each experiment is run 10 times, and the average value is taken to avoid randomness. The training epoch is 100, and the learning rate is 0.001.  
The SSL-VCST model can use unlabeled samples for auxiliary training of the network, and the rest of the models are trained using labeled samples. The average accuracy of different models under four task conditions is shown in Figure 7.  
It can be seen that the accuracy of SSL-VCST is superior to other models in all four sets of tasks. In task T1, with the fewest labeled samples, the accuracy of Swin-T and VCST is lower than 0.9. However, the SSL-VCST model can reach 0.902. This indicates that even with a small size of labeled samples, SSL-VCST can still achieve high accuracy in the fault diagnosis of rolling bearings. With the gradual increase in labeled samples, the accuracy of each model improves, and SSL-VCST maintains the highest accuracy. In task T4, when the training samples are sufficient, the accuracy of each model exceeds 0.98, and the accuracy of SSL-VCST can reach 1. It can be proved that the SSL-VCST model can adapt to different  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Size** & **T1** & **T2** & **T3** & **T4** \\ \hline Labeled samples & 100 & 200 & 500 & 1000 \\ Unlabeled samples & 1000 & 1000 & 1000 & 1000 \\ Test set & 500 & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental dataset settings.  
Figure 7: Comparative results of average accuracies of three models in four diagnostic tasks.  
sample situations and achieve a more stable accuracy with better generalization ability and stability.  
Applicability Experiments on Imbalanced SAMPLES and Insufficient Labeled Samples under Multiple Operating Conditions  
In the experimental-verification process in this section, the data-dividing method is consistent with Section 3.3.