Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis. (4th March 2021)

• Record Type: Journal Article
• Authors: Jin, Yanrui; Feng, Qiang; Zhang, Xiping; Lu, Peili; Shen, Jiaqi; Tu, Yihui; Wu, Zhiquan
• Other Names: Zhou Junlong guestEditor.; Kritikakou Angeliki guestEditor.; Zhu Dakai guestEditor.; Lastra Jose L. Martinez guestEditor.; Hu Shiyan guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 11(2021)
• Page Start: 2128
• Page End: 2142
• Publication Date: 2021-03-04
• Subjects: bearing fault diagnosis -- domain adversarial learning -- residual block -- sustainable wind turbine cyber‐physical system; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2937
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: As a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber‐physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA‐RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co‐design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA‐RNN can improve model performance. Meanwhile, DDA‐RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.