## 4 Experiments and Analysis of Results
### Case Study 1: CWRU Dataset
#### 4.4.1 Data Augmentation Model Evaluation
Figure 10 further compares the generation results of ACGAN and MDFT-GAN through localized zooming. In the zoomed-in areas, while the overall style of the ACGAN-generated images resembles the original samples, the restoration of texture details and feature distribution is clearly inadequate. In contrast, the signal samples generated by MDFT-GAN not only closely match the original images in terms of detail but also demonstrate greater stability during the generation process, significantly reducing artifacts and noise interference. This highlights MDFT-GAN's ability to more accurately capture complex signal features, further validating its superiority in generation quality. To rigorously evaluate the sample generation capability of MDFT-GAN, this study employs multiple quantitative metrics to assess image quality and similarity to real images. As shown in Table 4 and Figure 11, MDFT-GAN achieves the highest SSIM in most categories, with an average value of 0.91, demonstrating superior structural consistency and texture restoration. In terms of PSNR, MDFT-GAN slightly trails WGAN-GP but outperforms other models. Notably, for complex categories such as Class 7 and Class 9, it achieves the highest PSNR, highlighting its strong generative capacity for intricate visual structures. Figure 9: Comparison with other generative models. Figure 12a,b illustrate the cosine similarity and Pearson correlation coefficients of different models, respectively. MDFT-GAN consistently achieves high cosine similarity across categories, significantly outperforming other models, particularly in Categories 4, 5, and 10. Regarding Pearson correlation coefficients, MDFT-GAN shows a strong correlation close to 1.0 in most categories, indicating a superior fit to the real data distribution. In \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Class Category**} \\ \cline{2-11}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
[18]1 & 0.85 & 0.81 & 0.75 & 0.76 & 0.84 & 0.78 & 0.74 & 0.84 & 0.81 & 0.86 \\
[18]2 & 29.32 & 28.66 & 29.23 & 28.83 & 29.13 & 28.77 & 28.54 & 28.92 & 29.08 & 29.11 \\
[28]1 & 0.82 & 0.90 & 0.89 & 0.84 & 0.84 & 0.89 & 0.79 & 0.91 & 0.84 & 0.79 \\
[28]2 & 28.30 & 30.24 & 29.88 & 29.13 & 28.92 & 30.04 & 29.41 & 30.13 & 28.63 & 28.40 \\
[20]1 & 0.83 & 0.85 & 0.85 & 0.79 & 0.88 & 0.84 & 0.79 & 0.85 & 0.83 & 0.86 \\
[20]2 & 29.88 & 29.86 & 29.83 & 29.40 & 29.83 & 30.07 & 29.66 & 29.95 & 29.46 & 29.88 \\
[38]1 & 0.84 & 0.86 & 0.86 & 0.81 & 0.89 & 0.86 & 0.81 & 0.85 & 0.83 & 0.87 \\
[38]2 & 26.78 & 29.70 & 29.77 & 29.43 & 29.82 & 29.93 & 29.42 & 29.62 & 29.45 & 29.07 \\ Proposed1 & 0.92 & 0.90 & 0.92 & 0.90 & 0.89 & 0.92 & 0.90 & 0.89 & 0.92 & 0.90 \\ Proposed2 & 28.95 & 29.80 & 29.45 & 29.08 & 29.76 & 29.45 & 29.42 & 29.62 & 29.45 & 29.78 \\ \hline \hline \end{tabular}  
* Use of the SSIM evaluation indicator. 2 Use of the PSNR evaluation indicator.  
\end{table} 
Table 4: Comparison of evaluation indicators of the proposed model with other models. Figure 11: Comparison of different models. (**a**) Cosine similarity. (**b**) Pearson correlation. Figure 10: Comparison of MDFT-GAN and ACGAN generation results. contrast, GAN exhibits significant fluctuations in correlation values across multiple categories. Although ACGAN and DCGAN show improvement over GAN, they still fall short of MDFT-GAN, particularly in complex categories. To further evaluate the robustness of MDFT-GAN under limited sample conditions, experiments were conducted using different numbers of training samples, with results shown in Table 5. The experimental settings included 20, 30, and 40 training samples, and the SSIM and PSNR performance of MDFT-GAN was compared across these sample sizes. The results indicate that as the number of training samples decreases, the performance of MDFT-GAN slightly declines, but the overall change remains minimal, demonstrating strong stability.