## 3 Case Study
### SKAB Dataset
#### 3.2.2 Experimental Setup  
The experimental setup for the ATASML framework on the SKAB dataset adheres to a structured protocol designed to rigorously assess its performance under varied industrial fault scenarios. This setup is crafted to highlight the framework's robustness and adaptability through comprehensive experimental procedures involving both traditional and adversarial learning techniques.  
Data preprocessing for the SKAB dataset involves a sliding window technique, where data is segmented into windows of 30 consecutive data points each. This segmentation captures essential temporal dynamics and a step size of 15 points ensures adequate overlap and continuity between samples. Such preprocessing is crucial to ensure the data's suitability for complex anomaly detection tasks facilitated by the ATASML framework.  
This experimental setup on the SKAB dataset emphasizes a comprehensive assessment of the ATASML framework's performance under diverse industrial fault scenarios. Rather than focusing on few-shot fault classification, which is less representative of the SKAB dataset's unique characteristics and anomaly patterns, the approach here leverages a broader learning context. This involves employing extensive training datasets to thoroughly evaluate the framework's capabilities. Such an experimental design aims to robustly validate the generalization, accuracy and diagnostic precision of ATASML, particularly by showcasing detailed performance metrics throughout the training process, including accuracy improvements and loss reductions.  
In line with few-shot learning methodologies, the N-way K-shot approach is employed for constructing learning tasks. Here, 'N' distinct fault types are randomly selected from the dataset, and 'K' examples from each type are used to form the support set. Additional samples are used to construct the query set, culminating in approximately 10,000 training tasks and 1000 testing tasks. This sampling strategy ensures a comprehensive evaluation over a wide array of fault scenarios, thus providing a robust measure of the model's diagnostic capabilities.  
Adversarial training is integral to the ATASML approach, enhancing the model's ability to generalize from limited data and to recognize subtle anomalies indicative of faults. Strategic perturbations are introduced to the training data, forming adversarial samples that challenge the model's predictive accuracy. These perturbations are bounded by an \(\epsilon\)-ball centered around the original data points, with \(\epsilon\) set to 10% of the range of each feature, aimed at maximizing the prediction error and thereby simulating realistic fault conditions more effectively.  
The network architecture tailored for the SKAB dataset is designed to effectively harness both the feature-specific and temporal dynamics essential for accurate fault diagnosis. The architecture incorporates convolutional layers for initial feature extraction followed by LSTM units to capture long-term dependencies.  
The learning rates for the task-level (inner loop) updates are set at \(\alpha=0.01\), and for the meta-level (outer loop) updates at \(\beta=0.001\). The training involves preliminary adaptation through five inner update steps, followed by ten fine-tuning steps to refine the model parameters based on the insights gained from the adversarial samples, optimizing the model for high precision and reliability in fault detection.  
The configuration detailed in Tables 6 and 7 outlines the modular structure and specific parameters of the network, demonstrating the sequential application of convolutional and LSTM layers tailored to the unique requirements of the SKAB dataset. This design efficiently processes complex time-series data, effectively balancing feature extraction and temporal dependency modeling, which is critical for the continuous monitoring and anomaly detection tasks prevalent in industrial settings.