## 1 Introduction
However, this approach may not adequately address the significant shortage of target domain data in specific applications, particularly when failure samples are limited. [PERSON] et al. [7] proposed a fault diagnosis model based on feature migration, enabling domain adaptation of features and fault diagnosis across different devices. [PERSON] et al. [8] developed a fault diagnosis model that combines residual networks and transfer learning, reducing the time required for rolling bearing fault diagnosis. The combination of feature extraction and transfer learning enhances the model's sensitivity to fault features under varying conditions and improves both the accuracy and robustness of bearing fault diagnosis. However, in certain complex application scenarios, this method may involve a trade-off between the efficiency and accuracy of model training. [PERSON], [PERSON] et al. [9] explained that neural networks possess general approximation capabilities, enabling them to solve complex nonlinear problems more accurately than traditional statistical techniques, thus benefiting fault diagnosis. Several fault diagnosis cases are presented to demonstrate the effectiveness of neural networks in system fault analysis. Data imbalance is a prevalent and critical challenge in fault diagnosis tasks. [PERSON], [PERSON] [10] points to data imbalance as a situation where the number of samples in one class significantly exceeds that of other classes, causing the algorithm to favor the majority class, neglect the minority class, and lead to poor classification performance for the minority class. Although transfer learning has shown promising results, practical applications still face challenges such as mismatched data distributions, insufficient fault data samples, and low diagnostic accuracy. This paper employs a combination of random convolution layers and blind deconvolution to effectively handle noise and nonlinear features in the fault signal of the wind turbine spindle bearing, thereby enhancing the model's ability to adapt to data under varying operating conditions. The improved residual network model, integrated with the efficient channel attention mechanism, extracts fault features, enhances fault diagnosis accuracy, and addresses more practical scenarios. By combining transfer learning, this method effectively learns from limited training samples, overcoming the challenge of data scarcity in the target domain. Unity 3D, as a powerful game engine, is not only used for game development but also widely applied in industrial and scientific fields, particularly for visualization and data interaction. Unity 3D can create a 3D environment to visualize the working state and failure modes of the wind turbine spindle bearing. Through intuitive visualization, the damage characteristics of the bearings can be more effectively analyzed, and different fault scenarios can be simulated. Data interaction can occur between Unity 3D and local files, as well as between Unity 3D and databases. With the rapid advancement of information technology, digital twin technology offers a novel solution for the intelligent management of wind turbines. Digital twin technology enables interaction between virtual and physical spaces through the integration of models and data, allowing real-time reflection of the device's operating state, simulating its behavior, and providing digital support for the entire life cycle of its operation and maintenance. Digital twin technology can not only monitor and analyze wind turbine operations but also facilitate fault diagnosis, thereby enhancing preventive maintenance and management practices. The concept of the digital twin was proposed by [PERSON] in 2003, who developed a three-dimensional model incorporating physical entities, virtual space, and the communication of data between the two. Building on the three-dimensional digital twin model, [PERSON] et al. [11] introduced two additional dimensions\(-\)application services and twin data\(-\)and proposed the framework of the five-dimensional digital twin model.