Fault Diagnosis of Wind Turbine with Generative Adversarial Networks-based Oversampling Method


 Due to the complex working environment, the effective fault data of the wind turbine gears is often difficult to obtain. Aiming at this practical issue, a generative adversarial networks-based oversampling method is proposed in this paper, which can archive fault classification with small data set. At the initial stage, the wavelet packet transform (WPT) is applied to generate and extract features. Then, the optimal discriminator and generator trained by GAN are used to generate data to compensate for the imbalanced fault dataset. Random forest(RF), eXtreme gradient boosting (XGB) and support vector machines (SVM) are chosen to classify real dataset, imbalanced dataset and generated dataset separately. The experiment indicates that the data generated by the proposed method stays at the same distribution as the real data. Therefore, for small or imbalanced dataset situations, the proposed method can be a solution to compensate for the dataset.