## 4 Experiments and Analysis of Results
### Case Study 1: CWRU Dataset
#### 4.4.2 Fault Classification Model Evaluation  
To validate the effectiveness of the data generated by MDFT-GAN in classification tasks, t-SNE was employed to visually analyze the feature distributions generated by different models. The comparison results are presented in Figure 13 and Table 6, along with the calculated accuracy and F1 scores of the classification task. The calculation formulas are as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{26}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{27}\]  
Figure 13a-f show the t-SNE feature distributions of the data generated by each model. From the clustering patterns of the feature distributions, it is evident that the  
Figure 12: Comparison with different sample sizes. (**a**) Cosine similarity. (**b**) Pearson correlation. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Class Category**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
40 \({}^{1}\) & 0.87 & 0.87 & 0.85 & 0.89 & 0.90 & 0.86 & 0.89 & 0.82 & 0.86 & 0.89 \\
40 \({}^{2}\) & 30.12 & 30.07 & 29.77 & 29.47 & 29.18 & 30.09 & 29.45 & 29.68 & 29.62 & 29.15 \\
30 \({}^{1}\) & 0.86 & 0.90 & 0.89 & 0.84 & 0.88 & 0.87 & 0.81 & 0.85 & 0.87 & 0.85 \\
30 \({}^{2}\) & 28.88 & 29.12 & 29.13 & 28.95 & 28.91 & 29.07 & 28.59 & 28.94 & 29.03 & 28.76 \\
20 \({}^{1}\) & 0.86 & 0.87 & 0.87 & 0.79 & 0.85 & 0.85 & 0.80 & 0.83 & 0.84 & 0.82 \\
20 \({}^{2}\) & 29.36 & 29.27 & 29.15 & 28.95 & 28.92 & 29.07 & 28.79 & 28.78 & 29.09 & 28.70 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Use of the SSIM evaluation indicator. \({}^{2}\) Use of the PSNR evaluation indicator.  
\end{table} 
Table 5: The performance of MDFT-GAN in generating different training samples. data generated by MDFT-GAN exhibit compact clusters, with clear separation between classes and high consistency within classes. In contrast, the feature distributions generated by GAN and ACGAN show significant overlap, suggesting that the generated data lack distinguishability and are less representative of the real data. This indicates that the data generated by MDFT-GAN more accurately represent the true feature distribution and align closely with the real data. Table 6 describes the effect of data generated by different models on the fault classification performance. With 50 samples per category, the proposed MDFT-GAN model achieves a classification accuracy of 99.41, which is significantly better than the compared models. In addition, MDFT-GAN has the highest F1 score of 99.53, showing its excellent classification performance and cross-category equalization results. Even when the number of samples in each category is reduced to 30, MDFT-GAN still maintains a classification accuracy of 98.69 and an F1 score of 98.74, which further validates its robustness under limited samples. To validate the effectiveness of the proposed EH-ViT in generating data for classification tasks, this study compares its performance with several classical machine learning methods and deep learning models. The comparison methods include random forest (RaF), support vector machine (SVM), hierarchical CNN (HCNN), 2D-CNN, and 2D-ResNet. The \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Sample Size**} & \multicolumn{2}{c}{**Evaluation Metrics (\(\%\))**} \\ \cline{3-4}  & **per Category** & **Accuracy** & **F1** \\ \hline
[18] & 50 & 92.85 & 92.96 \\
[28] & 50 & 98.56 & 98.77 \\
[20] & 50 & 97.37 & 97.46 \\
[38] & 50 & 97.24 & 97.63 \\ Proposed & 50 & 99.41 & 99.53 \\ Proposed & 30 & 98.69 & 98.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance of data generated by different models. Figure 13: Comparison of t-SNE for different generated models. (**a**) GAN. (**b**) ACGAN. (**c**) WCGAN-GP. (**d**) DCGAN. (**e**) Proposed 50 Samples. (**f**) Proposed 30 Samples.