Fault diagnosis in wind turbines based on weighted joint domain adversarial network under various working conditions

• Published in: IEEE sensors journal Vol. 23; no. 13; p. 1
• Main Authors: Qi, Huaiyuan, Han, Yinghua, Tuo, Siwei, Zhao, Qiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: domain adaptation (DA); Fault diagnosis; Learning; partial transfer learning; SCADA; Supervisory control and data acquisition; Turbines; Weighting functions; Wind turbine; Wind turbines; Working conditions
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3279290

In recent years, the practical cross-domain wind turbine fault diagnosis is constrained by label space and distribution differences of fault data under different working conditions. Due to the difference in feature distribution, the generalizability of traditional transfer learning models can be affected. Moreover, the difference in label space, which refers to the target domain (SCADA data of another wind turbine), covers only a subset of the source domain (SCADA data of one wind turbine) fault categories, it reduces the accuracy of recognition by the diagnostic model for the target domain failure. Therefore, a weighted joint domain adversarial network (WJDAN) is proposed in this paper to overcome the above problems. In this method, a weighting function is introduced to identify and remove irrelevant source domain fault categories, with the contribution of source domain data to the domain discriminator, maximum mean discrepancy (MMD) and classifier measured. Besides, the domain adversarial network with the intraclass MMD loss is introduced to minimize the marginal and conditional distributional discrepancies between the source domain and target domain simultaneously. As revealed by the experiments on SCADA data of different wind turbines, the proposed WJDAN outperforms other traditional transfer learning methods on wind turbine fault diagnosis.