## 3 Experimental Analysis and Discussion
### Paderborn University Bearing Experiment
#### 3.3.1 Analysis of Fault Diagnosis Performance  
To comprehensively validate the effectiveness of the proposed method, the test set data were used to evaluate models trained on the four datasets. The test set is balanced, with 150 samples for normal and each fault category, totaling 900 samples. Four evaluation metrics--accuracy, precision, recall, and F1 score--were utilized to comprehensively \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Number of Classes**} & \multicolumn{3}{c}{**Normal Sample Number**} & \multicolumn{3}{c}{**Fault Class Sample Number**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-8}  & & **N** & **O** & **I** & **B** & **C** & \\ \hline E & 5 & 480 & 8 & 6 & 5 & 4 & 0.008–0.016 \\ F & 5 & 480 & 12 & 11 & 9 & 6 & 0.0125–0.025 \\ G & 5 & 480 & 40 & 33 & 27 & 20 & 0.042–0.083 \\ H & 5 & 480 & 96 & 82 & 65 & 48 & 0.1–0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the HUST datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Number of Classes**} & \multicolumn{3}{c}{**Normal Sample Number**} & \multicolumn{3}{c}{**Fault Class Sample Number**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-8}  & & **K001** & & **KA03** & & **KA07** & **KI01** & **KI03** \\ \hline A & 6 & 500 & 15 & 12 & 9 & 7 & 5 & 0.01–0.03 \\ B & 6 & 500 & 30 & 23 & 15 & 14 & 10 & 0.02–0.06 \\ C & 6 & 500 & 75 & 61 & 52 & 29 & 25 & 0.05–0.15 \\ D & 6 & 500 & 150 & 130 & 106 & 70 & 50 & 0.1–0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the PU datasets. measure the diagnostic performance of the model when dealing with imbalanced datasets. During the experiments, we first applied the KDE-ADASYN method to augment the samples in each dataset, achieving class balance. Next, we used the FFT technique to transform the time-domain signals into frequency-domain representations. The signals were then normalized to standardize their amplitude and eliminate any inconsistencies due to sensor characteristics. Finally, the preprocessed data were fed into the 1D-MobileNet network integrated with the SE block for training and evaluation. To ensure the reliability of the results, we conducted the experiments 10 times and averaged the metrics to obtain the final results. Table 5 presents the details of the PU dataset after applying the KDE-ADASYN method to augment samples. Compared with Table 3, the results clearly show that the state of class imbalance in the dataset improved significantly. Figure 6 shows the diagnostic effectiveness of the proposed method on each dataset. As shown in the figure, the proposed method exhibits outstanding diagnostic performance across various imbalanced datasets. In Dataset D, which has the least imbalance, the method achieved over 99% accuracy, precision, recall, and F1 score, demonstrating exceptionally strong classification performance. Although diagnostic performance decreases as the degree of imbalance increases, it remains high. Even in the extremely imbalanced Dataset A, the method achieved around 90% in accuracy, precision, recall, and F1 score. This fully demonstrates that the method has excellent fault diagnosis capabilities under various imbalance ratio fluctuations, indicating its applicability to real-world scenarios. To better illustrate the method's fault identification capabilities across various imbalanced datasets, we present the confusion matrix in Figure 7. The predicted labels 0, 1, 2, 3, 4, and 5 in Figure 7 correspond to K001, KA03, KA05, KA07, KI01, and KI03, respectively. The matrix demonstrates that the method performs exceptionally well in identifying faults in moderately imbalanced datasets, such as Datasets C and D, with accuracy rates exceeding 94%. However, in highly imbalanced datasets like Dataset A, while the overall