## 4 Experiments and Analysis of Results
### Case Study 1: CWRU Dataset
#### 4.4.1 Data Augmentation Model Evaluation  
To assess the data generation capability of the MDFT-GAN model under varying conditions, performance tests are conducted with limited training data, with sample sizes of 50, 40, 30, and 20 per category. A detailed analysis is presented for the case with 50 samples per category. Figure 7 shows the model training progression under this condition. The loss curve reveals that during the first 2000 epochs, the generator and discriminator engage in intense adversarial training, causing significant fluctuations in loss. Between 2000 and 4000 epochs, the generator persistently attempts to deceive the discriminator, while the discriminator adapts to more effectively distinguish generated data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Labels** & **Fault Category** & **Fault Width (Inch)** & **Sample Length** \\ \hline Class 1 & BF & 0.007 & 4096 \\ Class 2 & BF & 0.014 & 4096 \\ Class 3 & BF & 0.021 & 4096 \\ Class 4 & IRF & 0.007 & 4096 \\ Class 5 & IRF & 0.014 & 4096 \\ Class 6 & IRF & 0.021 & 4096 \\ Class 7 & ORF & 0.007 & 4096 \\ Class 8 & ORF & 0.014 & 4096 \\ Class 9 & ORF & 0.021 & 4096 \\ Class 10 & N & - & 4096 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CWRU dataset category settings. Figure 6: CWRU fault data collecting equipment. By 4000 to 6000 epochs, the system reaches a relatively stable state, indicating convergence. As illustrated in Figure 7, the discriminator's accuracy steadily improves throughout the training, approaching 1 by the end, demonstrating successful adaptation to the generated data and achievement of a steady state. Figure 8 presents the 10 classes of signal samples generated by the MDFT-GAN model after training under the 50-sample condition, alongside their corresponding original training samples. It is evident that the images generated by MDFT-GAN closely resemble the original samples in terms of texture, state, and feature distribution. The absence of noticeable artifacts or significant noise further demonstrates the model's exceptional signal generation capability and stability. Figure 8: Comparison of GT and MDFT-GAN generated samples. (**a**) GT. (**b**) MDFT-GAN. Figure 7: MDFT-GAN training on CWRU trends. (**a**) Training loss profile. (**b**) Accuracy curve of the discriminator. Figure 9 presents a qualitative comparison of generated samples across ten fault classes using four baseline generative models: GAN, ACGAN, DCGAN, and WCGAN-GP. Although these models can approximate coarse signal structures, clear discrepancies emerge in their ability to recover fault-discriminative features and maintain inter-class separability. GAN exhibits class-invariant high-frequency noise and texture collapse, especially in Classes 3 and 6, indicative of poor convergence and mode collapse. ACGAN alleviates some instability through label conditioning, yet suffers from oversmoothed representations in Classes 1 and 7, diluting crucial fault-specific frequency modulations. DCGAN introduces better periodicity, but its limited receptive field results in local inconsistency and spatial fragmentation--particularly visible in Classes 4 and 9--compromising semantic coherence. WCGAN-GP demonstrates improved noise suppression and global smoothness but lacks fine-grained detail restoration in Classes 2 and 8 due to the absence of cross-channel or contextual attention. These artifacts are not merely perceptual flaws; they reduce the fidelity of synthetic samples as training data and weaken their contribution to diagnostic learning. In contrast, the proposed MDFT-GAN effectively preserves both intra-class textural consistency and inter-class discriminative patterns as a result of its dual design: (i) multi-domain input encoding captures complementary signal characteristics across time, frequency, and time-frequency domains, and (ii) Transformer-based channel attention enhances global structural modeling while retaining fine detail. These design choices collectively enable the generation of diagnostically meaningful and visually faithful samples.