## 3 Experimental Analysis and Discussion
### Huazhong University of Science and Technology Bearing Experiment
#### 3.4.1 Analysis of Fault Diagnosis Performance  
To further verify the effectiveness and generalization performance of the proposed method, the model was trained using the imbalanced datasets E, F, G, and H from Huazhong University of Science and Technology and its performance was evaluated using the test set.  
Table 9 provides the details of the HUST dataset after applying the KDE-ADASYN method for sample augmentation. Compared to Table 4, the results clearly demonstrate a significant improvement in addressing class imbalance within the dataset.  
As illustrated in Figure 8, the evaluation metrics for all datasets exceed 90%, with Datasets F and G achieving metrics above 95%, and Dataset H's metrics nearing 100%.This result once again fully confirms the effectiveness and applicability of the proposed method. More importantly, compared to the results from the PU experiments, the diagnostic perfor  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Parameter Count** & **Memory Usage (MB)** & **Inference Time (s)** & **Acc (\%)** \\ \hline
1D-ResNet & 36,913 & 0.1408 & 2.78 & 98.84 \\
1D-CNN-G & 32,156 & 0.1227 & 2.51 & 98.51 \\
1D-CNN & 16,983 & 0.0648 & 2.04 & 98.78 \\ Inception-v2 & 28,038 & 0.107 & 2.38 & 98.83 \\
1D-ShuffleNet & 14,829 & 0.0566 & 1.83 & 99.21 \\ Proposed method & 2992 & 0.0114 & 1.01 & 99.72 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison between different methods using Dataset D.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Number of Classes**} & \multicolumn{2}{c}{**Normal Sample Number**} & \multicolumn{2}{c}{**Fault Class Sample Number**} & \multirow{2}{*}{\(\mathbf{p}\)} \\ \cline{3-4} \cline{6-7}  & & **N** & **O** & **I** & **B** & **C** \\ \hline E & 5 & 480 & 56 & 42 & 35 & 28 & 0.056–0.112 \\ F & 5 & 480 & 84 & 77 & 63 & 42 & 0.0875–0.175 \\ G & 5 & 480 & 280 & 231 & 189 & 140 & 0.292–0.583 \\ H & 5 & 480 & 480 & 480 & 455 & 336 & 0.7–1.0 \\ \hline \end{tabular}
\end{table}
Table 9: Details of the HUST datasets after applying KDE-ADASYN.  
mance in the HUST experiments is even more outstanding. We speculate that this might be related to the different ratio settings between the most and least fault categories in the two experiments. This finding validates that the degree of imbalance among minority classes, i.e., fault categories, also significantly impacts classifier performance, further confirming the research value of the issues raised in this paper.  
The confusion matrices in Figure 9 demonstrate the performance of the proposed method across Datasets E, F, G, and H. For the extremely imbalanced Dataset E, the method primarily exhibited classification errors between labels 3 and 4, while maintaining high recognition accuracy for other categories. As the sample imbalance decreases, misclassification between categories gradually diminishes. In Datasets F, G, and H, the recognition accuracy for each category exceeds 95%, with the highest surpassing 99%, highlighting the method's exceptional fault diagnosis capabilities and robust generalization ability.