## 3 Simulation Experiments
### Data Preprocessing  
Data preprocessing is a critical step in ensuring the performance of models, particularly when handling complex time-series data and equipment fault prediction tasks. In this study, detailed preprocessing of raw industrial operation data was conducted to enhance the model's adaptability to data features and its generalization ability to unseen data. The preprocessing steps include handling missing values, encoding categorical features, feature scaling, and label transformation, all aimed at optimizing data quality and laying a solid foundation for subsequent model training. During data loading, the dataset underwent a comprehensive examination to ensure its completeness and consistency. Missing values were addressed using the median imputation method. For intermittent missing data commonly found in industrial datasets, the median imputation method helps mitigate the influence of outliers on data distribution, ensuring data stability. By filling in missing values, the continuity of time-series data was preserved, preventing potential adverse effects on model training caused by missing entries. The median imputation method demonstrates strong generalizability and can effectively fill in missing information across different industrial datasets, particularly when handling sensor data with missing values [19]. The dataset also contained several categorical features (e.g., product ID and equipment type) that needed to be converted into numerical formats for deep learning models. This study employed label encoding, which maps each categorical feature to an integer value \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**UID**} & \multirow{2}{*}{**Product ID**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Air Temperature [k]**} & \multicolumn{2}{c}{**Process**} & \multicolumn{1}{c}{**Rotational**} & \multicolumn{1}{c}{**Torque**} & \multicolumn{1}{c}{**Tool Wear**} \\  & & & & **Temperature [k]** & **Speed [rpm]** & **[Nm]** & **[min]** \\ \hline
1 & M14860 & M & 298.1 & 308.6 & 1551 & 42.8 & 0 \\
2 & L47181 & L & 298.2 & 308.7 & 1408 & 46.3 & 3 \\
3 & L47182 & L & 298.1 & 308.5 & 1498 & 49.4 & 5 \\
4 & L47183 & L & 298.2 & 308.6 & 1433 & 39.5 & 7 \\
5 & L47184 & L & 298.2 & 308.7 & 1408 & 40 & 9 \\
6 & M14865 & M & 298.1 & 308.6 & 1425 & 41.9 & 11 \\
7 & L47186 & L & 298.1 & 308.6 & 1558 & 42.4 & 14 \\
8 & L47187 & L & 298.1 & 308.6 & 1527 & 40.2 & 16 \\
9 & M14868 & M & 298.3 & 308.7 & 1667 & 28.6 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Partial real predictive maintenance data. rather than its original string representation. Label encoding is widely applicable in industrial equipment fault prediction and effectively handles various types of categorical data [20]. To ensure comparability across features on the same scale, Min-Max scaling was applied to numerical features, normalizing their values to the [0, 1] range. By normalizing the features, all attributes were optimized on a uniform scale. Feature scaling is a commonly used technique for processing various time-series data, including physical quantities such as machine temperature, rotational speed, current, and voltage. Proper scaling ensures the model can efficiently utilize information from each feature. For target labels, One-Hot encoding was employed, transforming each category label into a binary vector. One-Hot encoding effectively converts discrete categorical data into a numerical format that can be understood by the model. In industrial equipment fault prediction, different fault types often have distinct characteristics. By using One-Hot encoding, the model can process each fault category independently, thus improving prediction accuracy and classification performance [21]. The preprocessing steps adopted in this study exhibit strong generalizability. The imputation techniques, feature scaling, and encoding methods utilized here are not only suitable for this dataset but can also be applied to other time-series fault datasets.