Research on Wind Turbine Main Shaft Bearing Fault Diagnosis Method Based on Unity 3D and Transfer Learning

In the field of wind turbine spindle bearing fault diagnosis, real-time monitoring of its operation is challenging. The state monitoring visualization is limited, fault data and sample labels are scarce, and fault data distribution varies under different operational conditions, leading to low diagnosis accuracy and slow diagnosis speed. To address these challenges, a wind turbine spindle bearing fault diagnosis method based on Unity 3D and transfer learning is proposed. Based on the characteristics of the wind turbine spindle bearing structure and operation, a digital twin model is established. The twin data transmit the necessary information to each module in various file formats. Additionally, the signal processing method (RB), combined with a random convolution layer and blind deconvolution, is employed to enhance the diversity of fault features. The processed signal is then fed into an improved residual network model with an efficient channel attention mechanism. Finally, the model incorporates related alignment and joint maximum mean difference for fault diagnosis. This model not only improves the extraction of key features but also adapts to edge and condition distributions through domain adaptation, enabling cross-domain identification. The digital twin system is implemented in Unity 3D, incorporating functions such as user login, wind turbine spindle bearing state monitoring, fault diagnosis, and fault warning, demonstrating practical applicability. Experimental results validate the effectiveness and superiority of the proposed method in fault diagnosis across various transfer learning tasks.