## 3 Case Study
### SKAB Dataset
#### 3.2.4 Advanced Training Dynamics and Stability Analysis  
To further substantiate the efficacy of the ATASML framework, a detailed analysis of the training process on the SKAB dataset is presented, specifically focusing on the 3-way 6-shot configuration. This configuration was chosen due to its balanced challenge in fault classification, providing insights into the model's performance under moderately complex scenarios.  
The experiments were configured with a batch size of 64 and an extended training duration of up to 20,000 epochs to ensure robust model convergence. The Adam optimizer was employed with a learning rate of \(5\times 10^{-4}\), slightly higher than typical settings to accommodate the complex nature of the SKAB dataset. This setup aims to rigorously test the adaptability and learning efficiency of the ATASML framework under varied fault conditions.  
Two key performance metrics were tracked throughout the training process: Diagnostic Accuracy (%) and Classification Loss (expressed in \(e-2\) scale). The training dynamics are illustrated in two parallel graphs, aligning with the methodology applied for the TEP dataset, enabling a direct comparison between the datasets.  
1. Diagnostic Accuracy (%): As depicted in Figure 11, the accuracy improves steadily with epochs and approaches a stability phase between 17,000 to 18,000 epochs, eventually reaching an optimal accuracy of 94.79%. This gradual stabilization, later than observed in the TEP dataset experiments, reflects the SKAB dataset's increased complexity and diversity in fault scenarios.
2. Classification Loss: The loss reduction trajectory, shown in Figure 11, indicates that the model efficiently learns to minimize error over extended epochs, substantiating the algorithm's capability to handle complex anomaly detection tasks effectively.  
These findings are pivotal when juxtaposed with the TEP dataset outcomes, where the model achieved stability quicker. The extended stabilization period in the SKAB dataset underscores the intricate fault patterns and the diverse operational conditions simulated within this dataset. Such comparisons not only demonstrate ATASML's robust performance but also its versatility and scalability across different industrial datasets.  
These results validate the efficacy of incorporating task sequencing and adversarial examples in the training process, as ATASML not only improves on basic accuracy metrics but also enhances precision and F1 scores significantly. This performance uplift is crucial in scenarios where even minor faults need to be detected reliably to prevent potential operational failures.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **3-way 5-shot** & **3-way 6-shot** & **8-way 5-shot** & **8-way 6-shot** \\ \hline MAML Accuracy & 71.44 \(\pm\) 2.3 & 72.33 \(\pm\) 2.3 & 63.16 \(\pm\) 2.0 & 64.06 \(\pm\) 2.0 \\ GOPML Accuracy & 84.18 \(\pm\) 1.3 & 84.67 \(\pm\) 1.4 & 74.32 \(\pm\) 1.2 & 74.80 \(\pm\) 1.2 \\ ATASML Accuracy & 94.25 \(\pm\) 0.9 & 94.79 \(\pm\) 0.9 & 84.01 \(\pm\) 0.8 & 84.55 \(\pm\) 0.8 \\ MAML F1 & 68.50 \(\pm\) 2.7 & 69.48 \(\pm\) 2.8 & 61.60 \(\pm\) 2.5 & 62.58 \(\pm\) 2.5 \\ GOPML F1 & 80.03 \(\pm\) 1.6 & 80.74 \(\pm\) 1.6 & 71.71 \(\pm\) 1.4 & 72.43 \(\pm\) 1.4 \\ ATASML F1 & 92.37 \(\pm\) 1.1 & 92.89 \(\pm\) 1.1 & 82.33 \(\pm\) 1.0 & 82.86 \(\pm\) 1.0 \\ MAML Precision & 73.09 \(\pm\) 3.6 & 73.17 \(\pm\) 3.6 & 64.71 \(\pm\) 3.2 & 64.78 \(\pm\) 3.2 \\ GOPML Precision & 86.02 \(\pm\) 2.6 & 85.62 \(\pm\) 2.6 & 75.51 \(\pm\) 2.3 & 75.11 \(\pm\) 2.3 \\ ATASML Precision & 91.42 \(\pm\) 1.6 & 91.95 \(\pm\) 1.7 & 81.49 \(\pm\) 1.5 & 82.01 \(\pm\) 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fault classification results with fluctuation on the SKAB dataset.