## 3 Case Study
### TEP Dataset Analysis
#### 3.1.2 Experimental Setup  
The ATASML framework leverages a structured experimental protocol to evaluate its performance under varied fault-diagnosis scenarios presented by the TEP dataset. This process is detailed below, from the initial data manipulation to the configuration of the base network designed to process time-series fault data effectively.  
Each data sample in \(\mathcal{X}_{orig}\) undergoes a predefined augmentation process to simulate realistic operational noise and temporal variations. The data is segmented using a sliding window of 30 min, equivalent to 30 data points at 1 min sampling intervals, with a step size of 10 min (10 data points), enhancing the dataset with overlapping window segments for comprehensive coverage. Gaussian noise is added to each data segment to represent typical sensor inaccuracies and environmental fluctuations, using a zero-mean Gaussian distribution with a standard deviation of 5% to 10% of the variable range, which aligns with the common noise levels in process sensors. The samples are temporally warped by modifying the time indices using a uniformly distributed scaling factor from 0.8 to 1.2, which models varying process speeds and operational dynamics.  
Adversarial samples are strategically engineered to enhance the model's resilience by challenging its predictive capabilities. These samples are derived by introducing calculated perturbations into samples from \(\mathcal{X}_{gauss}\), \(\mathcal{X}_{warp}\) and \(\mathcal{X}_{orig}\), specifically designed to maximize prediction errors and test the model's robustness under adversarial conditions. Samples are perturbed within an \(\epsilon\)-ball centered around the original data points, with \(\epsilon\) set to 10% of the range of each feature. This perturbation is directed by the gradient of the loss  
Figure 5: The visualization of certain variables from Normal samples and IDV2 samples in the training subset.  
function (calculated via backpropagation), aiming to identify the most sensitive changes that increase the model's prediction error.  
The framework adopts an N-way K-shot methodology for task generation, crucial for evaluating its few-shot learning capabilities. Tasks are composed by randomly selecting N fault types where for each fault type, K examples are chosen to form the support set and another Q examples to construct the query set. This setup facilitates the evaluation of the model across different operational settings and fault conditions. Approximately 10,000 training tasks and 1000 testing tasks are generated to ensure comprehensive model training and robust validation against the diversified conditions depicted in the TEP dataset.  
The base network architecture for processing the TEP dataset within the ATASML framework includes layers specifically designed to capture and analyze temporal dependencies and features relevant for time-series fault diagnosis. Initial layers use 1D convolutions with 64 filters of kernel size 3 to extract short-term temporal features, followed by ReLU activation for non-linearity. Batch normalization is applied post-convolution to stabilize and accelerate the learning process, followed by max pooling to reduce feature dimensionality, enhancing model generalizability and computational efficiency. Long Short-Term Memory (LSTM) units (100 units) are employed to capture long-term dependencies across the input features, which is critical for accurately identifying fault patterns over time. The network culminates in a dense layer with softmax activation that classifies the input data into predefined fault categories, based on the learned features and temporal patterns.  
The specific parameters for each network module used in the TEP dataset analysis are detailed in Tables 3 and 4, which outline the configurations of convolutional, LSTM and dense layers to optimize fault classification performance.