Fault diagnosis of wind turbine bearing based on CNN-XGBoost

Abstract

When a wind turbine bearing fails, because the fault signal shows the characteristics of instability and non-linearity,it is difficult to extract the fault features artificially, resulting in a lower accuracy of the wind turbine bearing fault classification. In order to realize the automatic extraction of wind turbine bearing faults and improve the accuracy of fault classification, this paper proposes a wind turbine bearing fault identification method based on the combination of convolutional neural network and limit gradient lifting algorithm. First, convert the original bearing fault signal into a grayscale image. Then, the gray image is input to the convolutional neural network to automatically extract the feature of the fault. Finally, the limit gradient boosting algorithm optimized by the grid search algorithm is used as a classifier to classify the fault. The simulation results show that the method used in this paper can achieve a high rate of correctness of wind turbine bearing fault identification, indicating that this method is effective for wind turbine bearing fault diagnosis.