## 4 Experiments and Results
### Results
Overall, our method surpassed all other methods in both AUC and pAUC across the two datasets, demonstrating stronger fault detection capabilities. Particularly on the ToyConveyor dataset, EGBAD's AUC was only 32.40%, far lower than other methods, while our method achieved 80.05%, showcasing a significant performance advantage. Additionally, PAE exhibited stable performance on both datasets but was still slightly inferior to our method. AE and IDNN performed well on the ToyCar dataset but showed worse performance on the ToyConveyor dataset, suggesting potential adaptability issues across different datasets. In contrast, our method excelled on both datasets, demonstrating strong generalization and robustness. These results indicate that our method is more effective in detecting faults on the ToyADMOS dataset, which is more focused on smaller faults and more exact diagnoses, showing the high practical value of our model for real-world applications. The lack of on-site experiments to evaluate the model's performance in real-world application has always been a drawback of the current studies. To address this issue, we collected real-world data to conduct the test; the results are presented in Table 4. It can be seen that our method performed substantially better in both AUC and pAUC, achieving an AUC of 80.67% and a pAUC of 70.33%. Compared to the second-best PAE method, our approach outperformed it by 8.5 percentage in AUC and 7.06 percentage in pAUC. This highlights the significant superiority of our method in the detection of fault features in wind turbine blades. Through detailed comparison, it is evident that our method exhibits exceptional performance in real-world wind turbine blade fault detection, particularly in robustness under complex environments and adaptability to small-sample data, surpassing other methods. This provides reliable technical support for real-time fault monitoring and maintenance of wind turbine blades. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**ToyCar**} & \multicolumn{3}{c}{**ToyConveyor**} \\ \cline{2-5}  & **AUC** & **pAUC** & **AUC** & **pAUC** \\ \hline AE [35] & 80.09\% & 67.22\% & 72.68\% & 60.65\% \\ EGBAD [38] & 59.40\% & 52.60\% & 32.40\% & 47.70\% \\ IDNN [39] & 77.42\% & 62.64\% & 69.36\% & 58.58\% \\ ANP [40] & 72.50\% & 67.30\% & 67.00\% & 54.50\% \\ PAE [41] & 75.35\% & 69.70\% & 77.58\% & 61.37\% \\ Proposed & **82.56**\% & **71.18**\% & **80.05**\% & **66.75**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results on ToyADMOS test data, where the bold font indicates the best performance. Figure 11 illustrates the relationship between the performance and the number of parameters of different models. The horizontal axis represents AUC, the vertical axis represents pAUC, and the size of the bubbles corresponds to the number of model parameters. Both AUC and pAUC are key metrics for evaluating model performance. Higher values indicate stronger classification capability. The bubble size reflects model complexity, with smaller values indicating a more lightweight model. From the plot, it is evident that our method (green bubble, 1.7 M parameters) excels in both AUC and pAUC, positioned in the high-performance region at the top right. This indicates that it achieves superior performance while maintaining a moderate parameter count, demonstrating strong efficiency. In contrast, other models show certain shortcomings in either performance or efficiency. AE (blue bubble, 1.05 M parameters) has fewer parameters but lower AUC and pAUC compared to our method, indicating a weaker performance. EGBAD (orange bubble, 3.5 M parameters) has a larger parameter but only marginal performance improvement, with both AUC and pAUC still trailing behind our model. IDNN (gray bubble, 0.13 M parameters) has the fewest parameters but the worst performance, with extremely low AUC and pAUC, making it unsuitable for practical applications.