In order to solve the problem that the fault classification accuracy of the main bearing of the wind turbine is not high due to the unbalanced vibration signal data of the main bearing of the wind turbine under the background of noise, this article proposes a double-layer fault diagnosis model for the main bearing of the wind turbine that combines the auxiliary classifier generation adversarial network (ACGAN) and the deep residual shrinkage network (DRSN). First, the wind turbine main bearing data is sent into the ACGAN to learn the distribution features of fault data, and a particular type of fault data is generated to expand the original dataset to achieve balance conditions, and then the expanded dataset is sent to the DRSN to reduce noise to improve the fault classification accuracy. The simulation results show that, compared with the traditional deep learning model, the model proposed in this article can significantly improve the classification accuracy of the main bearing fault of wind turbines under noise conditions, and also has a strong diagnosis ability in a state of datasets with different loads.

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.