## 4 Results
### Model Update Strategy  
Throughout the entire operation period of the wind turbine generator, factors such as mechanical component aging, material performance degradation, and cumulative effects of environmental factors can cause gradual changes in system characteristics. These changes present a significant challenge to the long-term accuracy of the digital twin-based early warning model. To ensure that the virtual model continuously maintains an accurate mapping of the physical system throughout its service life, this study adopts a lightweight model update strategy based on incremental learning to continuously optimize the early warning model in the digital twin system. The core of this strategy lies in establishing a dynamic model performance monitoring and update mechanism. The system continuously tracks the deviation between the model's predicted output and the actual monitoring data. When the system detects that the average relative error within the sliding time window (default setting is 720 h, approximately 1 month) surpasses the predefined threshold of 3%, it automatically initiates the incremental learning process. This update process uses the typical operating conditions of the latest sliding time window as the new training set and updates the model's weight parameters based on the restricted gradient descent algorithm. This update method has significant advantages. On one hand, the regularization term maintains the stability of the original important parameters, ensuring that historical operating patterns are retained. On the other hand, the weight of the new data is dynamically allocated using a time decay function, with recent operating data having a higher learning weight. Additionally, a parameter update amplitude limit (single update not exceeding the original value by 10%) is set to avoid overfitting risks. This process is shown in Figure 18.  
Implementation of Intelligent Monitoring and Fault Prediction for the Gear Transmission System of Wind Turbine  
To validate the effectiveness and applicability of the proposed method, a digital twin system for intelligent monitoring and fault prediction of the wind turbine gear transmission system was developed using Unity3D (Unity 2021.2.7 flc1) as the core development framework. The system primarily includes 3D visualization of operational status data and intelligent early warning for abnormal states. By leveraging Unity3D's powerful graphics processing capabilities, various types of equipment operational data are presented in the form of dashboards, charts, curves, and 3D models to help users intuitively understand the system status. Abnormal states can be identified through an established threshold determination system. When the collected data exceeds the preset safety threshold, the system automatically activates a multi-level alarm procedure to alert operators of potential equipment anomalies. As shown in Figure 19, a visualization scene is constructed using the Unity3D engine. In this scene, the 3D geometric model of the wind turbine is seamlessly integrated with interactive controls (including function buttons and information pop-ups), ambient lighting, and other visual elements to create a highly realistic virtual O&M environment, thereby enhancing the visualization of the wind turbine's status monitoring. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **r2** \\ \hline CNN-LSTM-Attention & 0.619 & 0.937 & 0.987 \\ CNN-LSTM & 0.756 & 1.153 & 0.964 \\ LSTM-Attention & 0.932 & 1.324 & 0.951 \\ LSTM & 0.984 & 1.337 & 0.919 \\ CNN & 1.382 & 2.118 & 0.8699 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of different models on evaluation metrics. The data visualization module, as the core of the fault warning system for the wind turbine gearbox, reads data collected and stored locally by sensors to enable visual presentation of the data. Figure 20 shows the status of the geometric models of the entire wind turbine and its transmission system from different perspectives in Unity3D.