## 2 Related Works  
Numerous scholars and research institutions have extensively researched and explored the prediction and maintenance of abnormal conditions in wind turbine gear transmission systems. By analyzing vibration data, SCADA data, and other information from wind turbines, researchers employ techniques such as probability and statistics [5; 6; 7], machine learning [8; 9; 10], deep learning [11; 12; 13], and signal analysis processing [14; 15; 16] to analyze operational data and thereby achieve fault prediction in the gear transmission system. The Supervisory Control and Data Acquisition (SCADA) system, a standard configuration in modern large-scale wind turbines, collects massive amounts of operational data that comprehensively capture the operating status of key components. Consequently, fault prediction methods for wind turbine gear transmission systems based on SCADA data have garnered widespread attention from researchers. [PERSON] et al. [17] proposed a fault detection strategy based on high-frequency SCADA data and multi-linear principal component analysis for feature dimensionality reduction. This approach implements multi-class fault detection using support vector machines in nonlinear, high-noise environments, thereby  
Figure 2: Fault occurrence rate and downtime. verifying the feasibility of efficient monitoring based on the existing SCADA system. [PERSON] et al. [18] proposed an incipient fault diagnosis strategy based on SCADA data, utilizing support vector machines for anomaly detection trained exclusively on normal operation data. Experiments proved that this method effectively reduces the operation and maintenance costs of wind turbines. [PERSON] et al. [19] used SCADA data and machine learning techniques to predict gearbox temperature and employed an ensemble regression method to forecast severe gearbox failures, thus preventing catastrophic events. [PERSON] et al. [20] proposed a weakly supervised convolutional neural network-driven thresholding method that successfully predicted faults in a wind turbine transmission system. [PERSON] et al. [21] proposed a method using a gated recurrent unit neural network to detect the main bearing faults in wind turbines in advance by analyzing SCADA data. [PERSON] et al. [22] proposed a SCADA fault prediction method that combines principal component analysis with support vector regression. This approach successfully detected anomalies prior to the occurrence of faults by analyzing residual changes, offering a viable technical path for early fault warning. [PERSON] et al. [23] proposed a wind turbine fault prediction and condition monitoring method utilizing recurrent neural networks and long short-term memory networks. Experimental validation showed that this method outperforms existing machine learning algorithms in terms of prediction accuracy and practicality. [PERSON] et al. [24] proposed an unsupervised prediction method based on SCADA data, which successfully realized fault warning by means of healthy data and adaptive operating condition compensation. [PERSON] et al. [25] proposed an anomaly detection method based on a bidirectional long short-term memory network that effectively detects wind turbine faults using a sliding window strategy. This method successfully detected a fault of the same wind turbine by analyzing the normal data of a single wind turbine. [PERSON] et al. [26] proposed a prediction model based on a bidirectional recurrent neural network and sliding window residual analysis. By extracting features from SCADA data, they effectively realized fault prediction for wind turbines. In recent years, the application of digital twin technology to industrial equipment has seen rapid development. Numerous scholars have achieved real-time monitoring and visualization of large-scale equipment by constructing digital twin models [27; 28; 29; 30]. At the same time, digital twin technology has also begun to be adopted in the field of wind power equipment. [PERSON] et al.