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Constructing Condition Monitoring Model of Wind Turbine Blades

Author

Listed:
  • Jong-Yih Kuo

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Shang-Yi You

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Hui-Chi Lin

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Chao-Yang Hsu

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Baiying Lei

    (Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen 518037, China
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen 518037, China
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518037, China)

Abstract

Wind power has become an indispensable part of renewable energy development in various countries. Due to the high cost and complex structure of wind turbines, it is important to design a method that can quickly and effectively determine the structural health of the generator set. This research proposes a method that could determine structural damage or weaknesses in the blades at an early stage via a model to monitor the sound of the wind turbine blades, so as to reduce the quantity of labor required and frequency of regular maintenance, and to repair the damage rapidly in the future. This study used the operating sounds of normal and abnormal blades as a dataset. The model used discrete wavelet transform (DWT) to decompose the sound into different frequency components, performed feature extraction in a statistical measure, and combined with outlier exposure technique to train a deep neural network model that could capture abnormal values deviating from the normal samples. In addition, this paper observed that the performance of the monitoring model on the MIMII dataset was also better than the anomaly detection models proposed by other papers.

Suggested Citation

  • Jong-Yih Kuo & Shang-Yi You & Hui-Chi Lin & Chao-Yang Hsu & Baiying Lei, 2022. "Constructing Condition Monitoring Model of Wind Turbine Blades," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:972-:d:774221
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    References listed on IDEAS

    as
    1. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    2. Ehsan Mollasalehi & David Wood & Qiao Sun, 2017. "Indicative Fault Diagnosis of Wind Turbine Generator Bearings Using Tower Sound and Vibration," Energies, MDPI, vol. 10(11), pages 1-14, November.
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    Citations

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    Cited by:

    1. Hongyan Dui & Yulu Zhang & Yun-An Zhang, 2023. "Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    2. Diego Teran-Pineda & Karl Thurnhofer-Hemsi & Enrique Dominguez, 2023. "Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors," Mathematics, MDPI, vol. 11(6), pages 1-17, March.

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