## 4 Experiments and Analysis of Results
### Imbalanced Training Sample Evaluation  
To evaluate the generative ability of the MDFT-GAN model under data imbalance conditions, two imbalanced training sample settings were designed: (a) 4-classification: Under identical damage size conditions, the data volumes for different fault types are imbalanced. (b) 7-classification: Under varying combinations of damage sizes and fault types, the data volumes are imbalanced. The specific training data settings for these scenarios are detailed in Table 11.  
In the 4-class experiment, the dataset consists of four types: ballistic fault (BF), internal race fault (IRF), outer race fault (ORF), and normal (N). The damage size for each fault type is 0.007, and the sample sizes are 40, 30, 20, and 50, respectively. This reflects a significant imbalance in sample sizes between the different categories. In the 7-class experiment, the dataset is expanded to include combinations of two different damage sizes (0.007 and 0.021), covering the same three fault types (BF, IRF, ORF) and the normal state (N). In this setting, the data imbalance is more pronounced, with differences in the number of samples not only across fault types but also within the same fault type under different damage sizes. For example, the sample size for Ball Fault is 40 for a damage size of 0.007 but drops to 20 for a damage size of 0.021. This experimental design more closely resembles the uneven data distribution typically encountered in real-world industrial scenarios. Figures 19 and 20 show the images generated by the GAN family of models trained under four and seven classes of imbalance conditions. The results show that the proposed method significantly outperforms the other models in terms of detail restoration and global \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Comparison**} & \multicolumn{4}{c}{**Evaluation Metrics (\(\%\))**} \\ \cline{2-5}  & **Models** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Machine & RaF & 93.13 & 93.44 & 93.13 & 93.01 \\ learning & SVM & 95.63 & 95.82 & 95.63 & 95.60 \\ \hline \multirow{4}{*}{Deep learning} & HCNN & 92.50 & 93.12 & 92.50 & 92.45 \\  & 2D-CNN & 90.62 & 90.93 & 90.62 & 90.58 \\ \cline{1-1}  & 2D-ResNet & 98.12 & 98.22 & 98.12 & 98.12 \\ \cline{1-1}  & Proposed & 99.25 & 99.25 & 99.25 & 99.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of different classification models. consistency. The images generated by GAN and ACGAN exhibit significant blurring and loss of detail, whereas WCGAN-GP, although it improves the contrast, still falls short of restoring complex textures. In contrast, the images generated by MDFT-GAN not only closely resemble the ground truth in terms of texture details, but also maintain a high degree of consistency in terms of global structure. This shows that the method is able to take into account both detailed and global features when generating high-quality images. Figures 21 and 22 show the cosine similarity and Pearson correlation coefficient curves of the compared models, respectively. The results show that MDFT-GAN achieves high levels of cosine similarity and correlation in all categories, which emphasizes the excellent  
Figure 19: Comparison of images generated under 4-class unbalanced training conditions. Figure 20: Comparison of images generated under 7-class unbalanced training conditions. agreement between its generated image distributions and the real image distributions. Compared to other models, MDFT-GAN generates more stable samples with higher consistency with real image distributions. Figure 23a,b show the confusion matrix results of the proposed model for four- and seven-category experiments, respectively; in the four-category experiments, the classification results show 100 accuracy for all categories, despite the imbalanced sample distribution. In the more complex seven-category experiment, the data variance is more pronounced, but the proposed model still exhibits excellent classification performance.