## 4 Experiments and Results
### Results  
The evaluation results of our method and baseline models on MIMII are presented in Table 2 and Figure 9. As can be seen from the table and bar chart, there are significant differences in the performance of various methods on MIMII test data. Our method achievedthe highest AUC and pAUC values across all devices except for valve, demonstrating its superior performance in anomaly detection tasks. For example, on the pump device, our method attained an AUC of 80.11% and a pAUC of 66.53%, significantly outperforming other methods by over 4%. In contrast, the EGBAD method performed the worst, particularly on the Fan device, where its AUC was only 39.50% and pAUC was 59.40%, indicating substantial limitations in handling complex anomaly detection tasks. Other methods, such as AE, IDNN, ANP, and PAE, showed intermediate performance but performed notably well on certain devices. For instance, PAE achieved an AUC of 95.41% on the valve device, which is higher than that of our method. Additionally, the AUC values for the slider device were generally higher, suggesting that its anomaly detection task is relatively easier, while the pAUC values for the valve device exhibited greater fluctuations, indicating that detecting anomalies in certain regions of this device is more challenging. In summary, the proposed method demonstrates significant superiority in both AUC and pAUC metrics, proving its effectiveness in industrial equipment anomaly detection. In comparison, other methods' performance is suboptimal and may require further optimization to adapt to complex anomaly detection tasks. We also evaluate the performance of our model on the ToyADMOS dataset. According to the experimental results in Table 3 and Figure 10, our method achieved the best performance in both AUC and pAUC metrics on ToyCar, reaching 82.56% and 71.18%, respectively. Specifically, PAE achieved an AUC of 75.35% and a pAUC of 69.70%, slightly lower than our method. AE attained an AUC of 80.09%, close to our method, but its pAUC was 67.22%, which is the consequence of its simple pattern. IDNN achieved an AUC of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Fan**} & \multicolumn{2}{c}{**Pump**} & \multicolumn{2}{c}{**Slider**} & \multicolumn{2}{c}{**Valve**} \\ \cline{2-9}  & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** \\ \hline DCASE [35] & 65.15\% & 52.59\% & 72.00\% & 60.00\% & 84.00\% & 66.00\% & 66.00\% & 50.00\% \\ EGBAD [38] & 39.50\% & 59.40\% & 31.90\% & 51.10\% & 62.00\% & 53.40\% & 69.80\% & 57.30\% \\ IDNN [39] & 65.94\% & 52.48\% & 74.26\% & 62.20\% & 84.34\% & 65.48\% & 83.70\% & 62.02\% \\ ANP [40] & 69.20\% & 54.40\% & 72.80\% & 61.80\% & 90.70\% & 74.20\% & 86.90\% & 70.70\% \\ PAE [41] & 72.94\% & 54.37\% & 74.27\% & 62.01\% & 91.92\% & 74.39\% & 95.41\% & 81.24\% \\ Proposed & **79.12**\% & **61.34**\% & **80.11**\% & **66.53**\% & **92.67**\% & **75.06**\% & **92.15**\% & **79.98**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results on MIMI test data, where the bold font indicates the best performance. Figure 9: Evaluation results on MIMI test data. 77.42% and a pAUC of 62.64%, while ANP reached an AUC of 72.50% and a pAUC of 67.30%. In contrast, EGBAD had the poorest performance among all methods, with an AUC of 59.40% and a pAUC of 52.60%, which demonstrates it can hardly handle this kind of task. On the ToyConveyor dataset, our method still outperformed all others in both AUC and pAUC, achieving 80.05% and 66.75%, respectively, showing a strong generalization ability. PAE demonstrated stable performance with an AUC of 77.58% and a pAUC of 61.37%. AE attained an AUC of 72.68% and a pAUC of 60.65%, while IDNN achieved an AUC of 69.36% and a pAUC of 58.58%. ANP reached an AUC of 67.00% and a pAUC of 54.50%. Notably, EGBAD performed significantly worse on these datasets, with an AUC of only 32.40% and a pAUC of 47.70%, indicating substantial limitations in its performance on the ToyConveyor dataset.