Wind Turbine Blade Fault Diagnosis Method Based on Digital Twin and Convolutional Neural Network

This paper proposes a wind turbine blade fault identification method based on digital twin and convolutional neural network. Since there are few samples of wind turbine blade fault data, it is very necessary to use digital twin for simulation. In this paper, a 2WM equal-scale model is constructed and vibration data is used to model wind turbine blades, and the modeling accuracy is 95.4. The future blade failure situation is predicted by using vibration trend.