A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions

Abstract

The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Recently, due to various reasons such as difficulty in obtaining fault data and random changes in operating conditions, deep transfer learning has achieved great attention in solving mechanical fault diagnoses. In order to solve the problems of variable working conditions and data imbalance, a novel transfer learning method based on conditional variational generative adversarial networks (CVAE-GAN) is proposed to realize the fault diagnosis of wind turbine test bed data. Specifically, frequency spectra are employed as model signals, then the improved CVAE-GAN are implemented to generate missing data for other operating conditions. In order to reduce the difference in distribution between the source and target domains, the maximum mean difference (MMD) is used in the model to constrain the training of the target domain generation model. The generated data is used to supplement the missing sample data for fault classification. The verification results confirm that the proposed method is a promising tool that can obtain higher diagnosis efficiency. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) to test the effectiveness of the proposed model.