## 4 Experiments and Analysis of Results
### Case Study 2: JNU Dataset
#### 4.5.1 Data Augmentation Model Evaluation  
Figure 15 illustrates the loss function curve of the MDFT-GAN during training. In the initial stages, the generator's loss gradually decreases, while the discriminator's loss stabilizes, indicating that the MDFT-GAN has reached a dynamic equilibrium. Further demonstration of the model's discriminative ability is shown in Figure 15b by the discriminative accuracy curve. The figure shows that the discriminator's accuracy increases rapidly during the early stages of training and approaches 1.0, signifying its ability to effectively distinguish between generated and real data. This validates the effectiveness of the training process for the MDFT-GAN.  
Figure 16 presents images of vibration signals generated by (1) GAN, (2) ACGAN, (3) DCGAN, (4) WCGAN-GP, and (5) MDFT-GAN across eight categories, compared with the ground truth (GT). The visual comparison indicates that MDFT-GAN demonstrates  
Figure 14: JUN data acquisition equipment.  
Figure 15: MDFT-GAN training on JNU trends. (**a**) Training loss profile. (**b**) Accuracy curve of the discriminator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Labels** & **Fault Category** & **Speeds (r/min)** & **Sample Length** \\ \hline Class 1 & IRF & 600 & 4096 \\ Class 2 & REF & 600 & 4096 \\ Class 3 & ORF & 600 & 4096 \\ Class 4 & N & 600 & 4096 \\ Class 5 & IRF & 800 & 4096 \\ Class 6 & REF & 800 & 4096 \\ Class 7 & ORF & 800 & 4096 \\ Class 8 & N & 800 & 4096 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Jiangnan University dataset category settings.  
significant advantages in image detail preservation, texture distribution accuracy, and noise suppression, outperforming the other models. To be specific, images generated by GAN exhibit significant random noise and lower overall quality. ACGAN and WCGAN-GP improve the image quality partly, but still suffer from missing details and feature distortion. Compared to them, the images generated by MDFT-GAN are highly similar to real images in all categories. The generated signals have clear texture distribution and accurate feature details, showing excellent generation capabilities.  
Figure 17a,b show the cosine similarity and Pearson correlation coefficient results for each model, respectively. Compared with other models, MDFT-GAN performs relatively well in terms of performance. It means that the images generated by MDFT-GAN can accurately capture the feature distribution of different categories.  
To visualize the feature distribution of the generated data, t-SNE was used in Figure 18 to reduce the dimensionality of the generated data features. Figure 18a-f show that the MDFT-GAN generated data features have a compact distribution, with clear separation between classes, and effectively retain the structure of the real data features. The feature  
Figure 16: Comparison of the results of different data generation models.  
Figure 17: Comparison of different models. (**a**) Cosine similarity. (**b**) Pearson correlation.  
distributions generated by other models have intra-class mixing or inter-class overlap, and it is difficult to accurately distinguish them, especially at the category boundaries.