## 5 Experiments and Results
### Comparative Experiments with Deep Learning Models  
To further validate the advantages of the proposed model, we designed three groups of comparative experiments to assess the fault detection performance of different deep learning architectures without data augmentation. The three configurations tested are as follows:  
1. A CNN-LSTM classifier trained without GAN-based data augmentation;
2. A classifier using only CNN;
3. A classifier using only LSTM. All models were trained on the original (imbalanced) training set, while the test set remained fixed, consistent with the previous GAN-CNN-LSTM experiment. The network  
Figure 11: ROC curve and PR curve of the GAN-CNN-LSTM model. structures of each model were kept consistent with their respective components in the GAN-CNN-LSTM model, with only minor adjustments to input dimensions to satisfy CNN or LSTM input requirements. The final results are summarized in Table 5.  
Experimental results show that all three models achieved strong performance on the negative class (normal samples), with overall Accuracy around 95%. However, their performance on the positive class (fault samples) varied significantly, making this the primary focus of our analysis. As illustrated in Figure 12, we compared the GAN-augmented CNN-LSTM model with the above three models. After data augmentation, the classification metrics on fault samples significantly improved. Notably, Precision increased from 76.88% to 95.38%, indicating a substantial improvement in the model's ability to accurately identify fault samples. Recall improved by approximately 2%, and the F1-Score increased by about 11%, confirming that data augmentation greatly enhanced the model's detection capability for the positive class. This experiment strongly supports the effectiveness of using GAN-based sample generation to address the class imbalance problem. Additionally, a comparison among the three deep learning architectures shows that the CNN-LSTM model outperformed both CNN and LSTM when detecting fault samples, achieving an F1-Score of 80.86%, compared to 80.05% for CNN and 76.83% for LSTM. Although the CNN-only model performed similarly in terms of F1-Score, its Recall was approximately 14% lower than that of the CNN-LSTM model, indicating limited sensitivity to fault samples. The LSTM model exhibited a similar limitation. Figure 12: Performance comparison of deep learning models on fault detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Label** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{2}{*}{CNN-LSTM} & 0 & 97.08\% & 95.01\% & 96.03\% & \multirow{2}{*}{93.43\%} \\  & 1 & 76.88\% & 85.28\% & 80.86\% & \\ \hline \multirow{2}{*}{CNN} & 0 & 94.70\% & 98.57\% & 96.60\% & \multirow{2}{*}{94.19\%} \\  & 1 & 90.68\% & 71.65\% & 80.05\% & \\ \hline \multirow{2}{*}{LSTM} & 0 & 94.65\% & 97.08\% & 95.85\% & \multirow{2}{*}{92.96\%} \\  & 1 & 82.67\% & 71.75\% & 76.83\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative classification results of deep learning models. To further evaluate the models' performance across different classification thresholds, we plotted the Precision-Recall (PR) curves and calculated the Average Precision (AP) for each model. As shown in Figure 13, the PR curve of the GAN-CNN-LSTM model encloses the largest area, with an AP of 0.9745, significantly higher than the other three models. The CNN-LSTM, CNN, and LSTM models achieved AP values of 0.9026, 0.9086, and 0.8710, respectively, with the LSTM model performing the worst. These results further demonstrate the effectiveness of data augmentation in improving model stability and generalization capability. In particular, when dealing with imbalanced fault detection tasks, the GAN-CNN-LSTM model not only achieves superior F1-Score, Precision, and Recall at a fixed threshold, but also shows a clear advantage in overall classification ability as reflected by the PR curve.