Wind turbine generator bearing early warning method based on feature fusion

The invention discloses a wind turbine generator bearing early warning method based on feature fusion. The method comprises the following steps: preprocessing CMS data; obtaining a time domain characteristic index; obtaining trend characteristic indexes; obtaining a frequency domain characteristic index; obtaining envelope characteristic indexes; carrying out feature fusion; training an extreme gradient lifting model; training a function set in the classification model, and constructing a learning objective function of XGBoost; and calculating the learning objective function of the XGBoost. According to the technical scheme, the vibration data of the CMS state monitoring system is utilized, the generator bearing fault mechanism is analyzed from the time domain feature, the trend feature, the frequency domain feature and the envelope feature, the four features are fused, the feature vector representing the running state of the generator is effectively extracted, the method greatly improves the recognition sens