Semi-supervised broad learning generative adversarial network for intelligent fault diagnosis of wind turbine based on SCADA data

Abstract

SCADA data-driven fault diagnosis is a research hotspot in the field of wind turbine intelligent fault diagnosis. However, most existing methods rely on labeled data and neglect to mine fault information from a large amount of unlabeled data. Moreover, the common distribution information between labeled and unlabeled data is not well considered. To solve these problems, a semi-supervised broad learning generative adversarial network (SSBAN) is proposed for wind turbine intelligent fault diagnosis. First, the SSBAN is established through an integrated combination of BLS and GAN discriminator. Second, labeled data participates in multi-classification task for classification, while unlabeled data and simulated data generated based on labeled data are used for binary-classification task to distinguish between real data and generated data. Finally, two classification tasks share parameters to extract the shared distribution information from both labeled and unlabeled data, reduce over-reliance on labeled data, and enhance model performance. Two experiments are carried out to verify the effectiveness of the proposed method, and the results show that the proposed SSBAN can leverage the role of unlabeled data to improve diagnostic performance.