A wind turbine bearing fault diagnosis method based on multi-feature integration and extreme gradient boosting（XGBoost） is proposed in addressing the insufficient describing bearing vibration information from time domain

frequency domain and time-frequency domain separately

and the low accuracy in traditional fault diagnosis algorithms. Firstly

the time domain

frequency domain and time-frequency domain characteristics of the bearing vibration signal are extracted. Then XGBoost algorithms is used to diagnose the bearing faults. The information gain of each feature in tree node splitting is calculated

and features are selected according to the information gain. Finally

using support vector machine（SVM）

K-nearest neighbor（KNN）

artificial neural network（ANN） and other algorithms to conduct fault diagnosis and comparison experiments on the selected features. The simulation results show that the proposed fault diagnosis algorithm can extract features with high distinction and independence

and is superior to SVM

KNN

ANN and other algorithms in bearing fault diagnosis rate.