Fault diagnosis of wind turbines with generative adversarial network-based oversampling method

Abstract

Due to the complex working environment, effective fault data from wind turbine gears are often difficult to obtain. Aiming at this practical issue, a generative adversarial network (GAN)-based oversampling method is proposed in this paper, which can achieve fault classification with a small dataset. In the initial stage, wavelet packet transform 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, eXtreme gradient boosting and support vector machines are chosen to classify a real dataset, imbalanced dataset and generated dataset, respectively. Experiments indicate that the data generated by the proposed method stay at the same distribution as the real data. Therefore, for small or imbalanced dataset situations, the proposed method could be a solution to compensate for the dataset.