## 3 Case Study
### TEP Dataset Analysis  
#### 3.1.1 Dataset Overview  
The Tennessee-Eastman process (TEP), as described in seminal works by [PERSON] and [PERSON] [41; 42], serves as a fundamental resource for the study of chemical process fault diagnosis. This dataset models a chemical production process and is instrumental in the development and benchmarking of diagnostic algorithms. Illustrated in Figure 4, TEP is structured around five critical subsystems: a reactor, a condenser, a vapor-liquid separator, a recycle compressor and a product stripper. It offers a rich array of data, with each sample comprising 52 features derived from 41 process measurements and 11 manipulated variables, captured at three-minute intervals. The dataset is segmented into sets representing normal operations and various fault conditions, designed to challenge and evaluate fault-diagnosis methodologies. Specifically, the normal operation dataset encompasses 500 samples, while the fault dataset is detailed into 21 distinct conditions, each represented by 480 samples in the training set. For testing and validation, an additional subset is provided, featuring 960 samples per fault condition, where fault signatures are introduced following an 8 h operational baseline. This structured approach allows for comprehensive training and rigorous testing of diagnostic models like ATASML, underlining the dataset's significance in advancing fault-diagnosis research. Given the limited documentation for the final six fault types within the TEP dataset, this study focuses on the analysis and diagnosis of the initial 15 fault categories. Table 2 provides detailed insights into these fault categories, highlighting their distinctive characteristics. These selected faults encompass a broad range of operational discrepancies, each presenting unique diagnostic challenges. Figure 4: Flowsheet of the Tennessee Eastman process. In particular, Figure 5 illustrates the comparative visualizations of certain process variables under normal operational conditions and during Incident of Disturbance Variable 2 (IDV2) within the training subset. This visual representation aids in understanding the fault's impact on the process dynamics and the complexity of distinguishing fault conditions based on process data. Traditional fault-diagnosis techniques often stumble in scenarios where limited or no training data for specific faults is available, underscoring the necessity for innovative approaches capable of learning from sparse datasets. In this context, the application of the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework represents a significant advancement. ATASML's methodology, designed to enhance diagnostic accuracy even in data-constrained situations, proves to be especially valuable. By leveraging a meta-learning paradigm and incorporating adversarial task augmentation, ATASML demonstrates enhanced adaptability and efficacy in fault diagnosis across a wide spectrum of fault conditions, as evidenced by its performance on the first 15 faults of the TEP dataset. Given the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework's unique approach to fault diagnosis, the preprocessing of the TEP dataset was tailored to complement its learning strategy. To facilitate this, a sliding window technique was employed, carefully segmenting the dataset to capture the intricate operational dynamics over time. This preprocessing step involved creating windows comprising 40 consecutive data points, corresponding to a 2 h operational window, with a step size of 20 points to ensure continuity and temporal relevance in the captured data. This data segmentation method is crucial for ATASML's ability to discern and learn from the nuanced variations and patterns within the process data. It allows the algorithm to effectively utilize each data window, ensuring that the full spectrum of operational dynamics is considered during the learning phase.