## 3 Case Study
### SKAB Dataset  
#### 3.2.1 Datasets Description  
The Skoltech Anomaly Benchmark (SKAB) [46] serves as a pivotal resource for anomaly detection within simulated industrial environments, reflecting real-world operational challenges. This dataset encompasses a range of subsystems monitored by various sensors, capturing data on parameters such as temperature, pressure and flow rates. SKAB consists of 34 time-series datasets, each representing a different operational scenario within an industrial process. One dataset is designated as anomaly-free to establish a baseline for normal operations, while the remaining 33 datasets exhibit transitions from normal to anomalous states, simulating faults within a pumping system due to induced anomalies (Figure 9). Anomalies are integrated into the datasets to mimic faults from diverse sources, including valves and pumps. These faults specifically affect the fluid dynamics, manifesting as subtle yet detectable anomalies aimed at testing the limits of diagnostic algorithms. The anomalies are present in both training and testing subsets, ensuring comprehensive exposure to varied conditions. The SKAB dataset provides a comprehensive set of fault scenarios, as visualized in Figure 10. These scenarios simulate fluid leaks and additions,  
Figure 8: Comparison of diagnostic accuracy under different batch sizes and learning rates for the 8-way 5-shot configuration. (**a**) Impact of batch size on diagnostic accuracy. (**b**) Impact of learning rate on diagnostic accuracy. The circles in (**b**) represent the results from ten iterations conducted at each learning rate, illustrating the variability and statistical reliability of the measurements. highlighting the complexity and variability of anomaly patterns within the dataset, which challenge the detection algorithms. The ATASML algorithm requires a methodically preprocessed dataset to perform efficiently. For the SKAB dataset, preprocessing involved the application of a sliding window technique, segmenting the dataset into windows of 30 consecutive data points each. This window size was strategically chosen to capture significant operational dynamics, with a step size of 15 points to maintain overlap and continuity. Each window represents  
Figure 10: Visualization of typical fault scenarios in SKAB, simulating fluid leaks and additions. This highlights the complexity and variability of anomaly patterns within the dataset, challenging the detection algorithms. Figure 9: Configuration and composition of the water circulation, control, and monitoring systems in the SKAB dataset: Front panel and composition of the water circulation, control, and monitoring systems: 1—water tank; 2—water pump; 3—electric motor; 4—solenoid valves; 5—mechanical lever for shaft misalignment; 6—emergency stop button. Sensors: 7—flow meter (NI 9401 8-channel); 8—water pressure meter (NI 9203 8-channel); 9, 10—vibration sensors (NI 9232 3-channel); 11, 12—thermocouples (NI 9213 Spring Terminal 16-channel thermocouple) [46]. an individual learning task, forming a structured dataset \(D=\{(x^{(j)},y^{(j)})\}\), where \(x^{(j)}\) denotes the input features and \(y^{(j)}\) the corresponding labels, which classify the operational state as normal or anomalous. This task-specific dataset formulation is critical for training the ATASML algorithm, which enhances its capability to recognize and adapt to new and complex anomaly patterns effectively. ATASML's application capitalizes on advanced meta-learning strategies that utilize both inherent data features and artificially induced anomalies to train robust anomaly detection models. By incorporating adversarial samples during the training phase, ATASML enhances the model's adaptability and sensitivity to subtle operational changes, which are often indicative of potential faults. The algorithm's structured learning approach, enriched with sequential and adversarial training components, provides significant advantages over conventional models.