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A Novel Wind Turbine Health Condition Monitoring Method Based on Correlative Features Domain Adaptation

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Abstract

Aimed at the difficulty in fault diagnosis of wind turbine transmission system under variable working conditions, the paper proposes a novel health condition monitoring method based on correlative features domain adaptation. Firstly, the envelope analysis of the collected signals is carried out, and the time–frequency features of the signals are extracted to construct the feature set. The feature sets under the similar working conditions to target are selected as the auxiliary sample sets in source domain through the transferability evaluation. Then, a transformation matrix is found to adapt the marginal and conditional distributions of wind turbine sample data under different working conditions, and its weight is adjusted. While reducing the discrepancy between domains, the class imbalance problem is taken into consideration, so as to improve the accuracy of fault diagnosis under the target working condition. Finally, the classifier is trained with the adjusted source domain and tested in the target domain. Experiments show that the proposed method can effectively improve the accuracy of wind turbine fault diagnosis.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant no. 51505202), the Natural Science Foundation of Jiangsu Province of China (no. 2020), the 333 Project of Jiangsu Province (2016-III-2808), the Qing-Lan Project of Jiangsu Province (QL2016013). We are also grateful to the editor-in-chief and reviewers for their kindly work to improve this paper.

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Authors and Affiliations

  1. School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221116, People’s Republic of China

    Wenyi Liu, He Ren, Mirza Ali Shaheer & Jahanzeb Aslam Awan

  2. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Wenyi Liu

  3. School of Rail Transportation, Soochow University, Suzhou, 215131, People’s Republic of China

    He Ren

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  1. Wenyi Liu
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  2. He Ren
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  4. Jahanzeb Aslam Awan
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Correspondence to Wenyi Liu.

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Liu, W., Ren, H., Shaheer, M.A. et al. A Novel Wind Turbine Health Condition Monitoring Method Based on Correlative Features Domain Adaptation. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 191–200 (2022). https://doi.org/10.1007/s40684-020-00293-5

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