Abstract:To solve the problem that the fault data of wind turbine planetary gear box is scarce and difficult to extract, which leads to the low accuracy of the final fault identification, a fault diagnosis method combining dual attention mechanism and transfer learning was proposed. Firstly, the original vibration data of the planetary gear box are normalized and input into the convolutional neural network to extract the features. Then the feature maps are input into the location attention mechanism and channel attention mechanism respectively to extract advanced features. Finally, feature fusion is performed to output diagnostic results. In the case of variable condition migration, the source domain model is fine-tuned and the prediction category is output after the parameter migration to the target domain condition. The experimental results show that the fault identification accuracy of the proposed method after migration is above 98%, which is significantly improved compared with other models such as support vector machine (SVM) and limit gradient lift (XGBoost).