## 3 Simulation Experiments  
### Simulation Data  
To validate the effectiveness and superiority of the proposed hybrid model, simulation experiments were conducted to evaluate its performance. The experiments were implemented based on the TensorFlow deep learning framework. The detailed experimental environment configuration is presented in Table 1.  
The AI4I 2020 Predictive Maintenance Dataset was used as the test dataset for this experiment. This dataset reflects real-world predictive maintenance scenarios, consisting of a total of 100,000 samples, 8 features, and 6 labels. The 8 features include UID, product ID, air temperature, process temperature, rotational speed, torque, and tool wear. The 6 labels represent different types of failures, which include overall failure, tool wear failure, heat dissipation failure, power failure, overstrain failure, and random failure.  
Specific details of the fault types are summarized in Table 2.  
To validate the effectiveness of the proposed model, the dataset was divided into a training set and a testing set in a ratio of 80% to 20%. Specifically, 80,000 samples were  
\begin{table}
\begin{tabular}{c c} \hline
**Environment** & **Specifications** \\ \hline Operating System & Windows11 (Version 22H2) \\ CPU & 13 th Gen Intel(R) Core(TM) i9-13900 HX 2.20 GHz \\ GPU & NVIDIA GeForce RTX 4080 \\ Memory & 32 GB \\ Software & Visual Studio 2022 (Version 17.5.4) \\ Language & Python 3.9.12 \\ CUDA & 11.7.1 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.  
\begin{table}
\begin{tabular}{c c} \hline
**Fault Label** & **Description** \\ \hline Machine Failure & This indicates whether the equipment has experienced any failure. \\ Tool Wear Failure & Failure occurs when the equipment reaches the end of its tool wear lifespan or \\  & requires replacement. \\ Heat Dissipation Failure & Failure is caused by inadequate heat dissipation due to the equipment’s condition \\  & and temperature. \\ Power Failure & Failure occurs when the equipment’s power exceeds the power supply threshold. \\ Overstrain Failure & Failure is caused by excessive mechanical strain on the equipment. \\ Random Failure & Failure occurs due to random, unpredictable factors. \\ \hline \end{tabular}
\end{table}
Table 2: Fault descriptions.  
randomly selected for model training, while the remaining 20,000 samples were used to evaluate the model's performance. The datasets used for training and testing are contiguous in the time domain. This partitioning strategy ensures that the training data are sufficient for the model to learn effectively, while also leaving enough samples in the testing set to comprehensively assess the model's generalization ability.  
During the training phase, the training set was used to optimize the model's parameters, ensuring that the model learns the mapping relationship between input features and output labels. The testing set, on the other hand, was utilized to evaluate the model's performance on unseen data, validating its practical applicability and robustness. The random partitioning of the dataset eliminates potential biases caused by manual selection, thereby ensuring the fairness and reliability of the evaluation process [17; 18].  
In the experimental design, multiple operational variables of equipment (e.g., temperature, rotational speed, torque, etc.) were monitored to enable fault prediction. Binary fault labels (0 indicating normal operation; 1 indicating fault occurrence) were employed as supervisory signals to guide the training and testing processes of the model, thereby evaluating its predictive capability and real-world performance. Table 3 presents information on a subset of samples from the dataset.