###### Abstract  
To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R\({}^{2}\)) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. digital twin; multi-source data fusion; wind turbine gear transmission system; fault early warning model 23 June 20252 August 20253 August  
vital in the global energy system due to its environmental friendliness and renewability. This shift not only reflects the great importance that the international community attaches to ecological protection but also embodies the strategic need to address climate change and achieve low-carbon development. Currently, wind power generation technology has become a fundamental pillar in reshaping the energy structure and achieving sustainable development goals. According to statistical data from the Global Wind Energy Council (GWEC) 2024 annual report, the global cumulative installed wind power capacity exhibited a steady upward trend over the past decade (2013-2023), as shown in Figure 1a. In 2023, the newly added global installed capacity was approximately 117 GW, an increase of 48.1% compared to 2022, raising the total global installed capacity to over 1017 GW. According to the latest statistics, as of the end of 2023, China's cumulative installed wind power capacity exceeded 440 GW, representing 43.7% of the total global installed capacity (as shown in Figure 1b). With the rapid growth of the global wind power industry, the challenges hindering the development of the industry are becoming increasingly prominent, leading to increased demands for the operational stability of wind turbines. As the core component for energy conversion in a wind turbine, the gear transmission system plays a crucial role in efficiently transforming the rotor's mechanical energy into electrical energy. Therefore, its reliability directly affects the operational efficiency of the entire power generation system. The gear transmission system, as an essential component of the wind turbine, is responsible for efficiently converting the rotor's mechanical energy into electrical energy. It not only needs to endure substantial dynamic loads from the rotor but also withstand complex alternating loads and extreme environmental conditions encountered during high-speed operation and frequent startups and shutdowns [1]. According to the fault statistics in Figure 2, the electrical system is most prone to faults; however, these faults typically lead to shorter downtime and are easier to repair. Although the gearbox and transmission system experience failures less frequently than the electrical system, such failures lead to longer downtime and higher economic losses [2].