## 4 Experimental Validation
### Case 1: Public Bearing Dataset of Paderborn University (PU)
#### 4.1.3 Evaluation and Analysis of Case 1  
Based on the previous analysis, model training is conducted on the public PU dataset according to the stated basic network structure parameters. Figure 7 shows the accuracy curves for different methods on the public dataset PU. Obviously, compared to ISCV-ViT and ISCV-ResNet50, ViT, which directly uses the time-domain diagram of single-phase current signals as input without processing, performs poorly in terms of both convergence speed and accuracy. This also directly proves the feasibility of the ISCV method for preprocessing current signals. Through further comparative observation, the accuracy of the ISCV-ViT is the highest, and there is a large gap between the ISCV-ViT and others. In this experiment, the input images of ViT are time-domain images of MCS that have not been processed by ISCV. Due to the high level of noise and complex data components, the highest diagnostic accuracy of ViT is only 69.4%. This can be explained as reducing the complexity of your dataset, which can potentially improve the accuracy of the deep learning \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Name** & **Fault Category** & **Extent of Damage** & **Combination** \\ \hline
0 & K001 & Normal & - & - \\
1 & KA30 & OR1 (outer ring) & 1 & R (repetitive damage) \\
2 & KB23 & IROR & 2 & M (multiple damage) \\
3 & KI04 & IR1 (inner ring) & 1 & M \\
4 & KA04 & OR2 & 1 & S (single damage) \\
5 & KI16 & IR2 & 3 & S \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bearing fault categories of the experiments. Figure 6: The test-rig of the bearing dataset of Paderborn University (PU). model [51; 52]. In addition, ISCV-ViT starts to stabilize after approximately 150 epochs due to the slightly slower training convergence of the ViT model on small and medium-sized datasets. By comparison, ISCV-ResNet50 converges very quickly after approximately 60 epochs have passed. This is a predictable result since the residual connection and batch normalization can accelerate the convergence of the network applied in ResNet50. Meanwhile, the performance of the three models is stable under two operating conditions with different load settings. This indicates that the accuracy and generalization of the ISCV-ViT are superior by far. To further quantitatively evaluate the diagnostic performance of the ISCV-ViT model, confusion matrices are created for the best validation results under different operating conditions, as shown in Figure 8. The corresponding accuracies are 95.4% for N15_M01_F10 and 97.1% for N15_M07_F10. Notably, it can be clearly seen from the confusion matrices that a small number of OR1 faults are misjudged as IR1 faults. Otherwise, almost all other fault prediction accuracies reached 100%. Simultaneously, different working conditions displayed the same misjudgments. Therefore, it can be concluded that the classification of IR and OR faults still has room for improvement. Meanwhile, it can be inferred that this misjudgment may be related to the damage combination of the faults. Finally, the data from repetitive experiments is further statistically analyzed. Generally, the ViT model is superior to the ResNet model in processing temporal sequences due to the attention mechanism, which can obtain global characteristics. The mean and standard deviation of the repeated experiment accuracy are calculated to describe the reliability of the model, and the experimental results shown in Figure 9 also prove this point. The average accuracy of the proposed ISCV-ViT method reached 95.40% and 96.60% for the two conditions, which is higher than that of the other methods. Additionally, the standard deviation of the ISCV-ViT is also lower than that of the others, which means that the stability of the ISCV-ViT is better. Table 5 supplements the experimental results of [50] on its own PU dataset by using features extracted from MCS.