Aiming at the problems of traditional boosting algorithm in dealing with wind turbines running big data

such as low efficiency

low accuracy and poor real-time performance

a fault detection method for wind turbine gearbox based on gradient boosting decision tree is proposed. Firstly

combining the expert experience and using the maximum information coefficient

the correlation between massive data and large data of wind turbines is analyzed

and feature selection is made to obtain reasonable eigenvectors based on this. Secondly

the Bayesian hyperparameter optimization algorithm is used to optimize the gradient boosting decision tree. The key parameters in the gradient boosting decision tree algorithm are used to establish a fault detection model based on the gradient boosting decision tree. Finally

the maximum information coefficient analysis is used as the input value of the fault detection model of the gradient lifting decision tree

and the type of the gearbox is used as the output value. The experimental results show that compared with other algorithms

this method has a lower missed alarm rate and false alarm rate and stronger generalization ability

which can detect faults in real time and reduce maintenance costs.