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In recent years, there were many studies on intelligent fault diagnosis of wind turbines based on vibration signals. There are also many algorithms for classification of failure categories. Such as SVM, ELM and Random Forest. Random forest is a new ensemble algorithm. In this paper, the dimension reduction of feature vector using PCA algorithm is proposed for gearbox vibration signals, which makes the training time of the model reduced. And then, it used GA to optimize the number of decision trees and the number of attributes in the split attribute set in the random forest combined classifier. Through comparative experiments, the effectiveness of the proposed method is proved. Introduction

Fault Diagnosis of Wind Turbine’s Gearbox Based on Improved GA Random Forest Classifier