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Research on crack detection method of wind turbine blade based on a deep learning method

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  • Xiaoxun, Zhu
  • Xinyu, Hang
  • Xiaoxia, Gao
  • Xing, Yang
  • Zixu, Xu
  • Yu, Wang
  • Huaxin, Liu

Abstract

For the propose of improving the economic benefits of wind turbine utilization, an image recognition model based on deep learning called ‘Multivariate Information You Only Look Once’(MI-YOLO) is proposed which can detect the surface cracks of wind turbine blade efficiently, especially for cracks with light colors. In order to improve the extraction ability of light color and low definition, the Multivariate Information fusion and the use of C3TR module are put forward. Alpha-IOU is used to balance the precision rate and recall rate of the new model, and further improve the mAP. Aim at solving the problem of small amount of data and unbalanced positive and negative samples, two new data enhancement methods are employed. The detection performance of the proposed method is tested using wind turbine’s blade images with cracks taken by Unmanned Aerial Vehicle (UAV). Results show that the MI-YOLO is not only lighter, but also has a higher mAP than the YOLOv5s. Meanwhile, the economic efficiency of the proposed method is analyzed and compared with other detection method with the limitations of the proposed method for offshore wind turbines also being discussed.

Suggested Citation

  • Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014982
    DOI: 10.1016/j.apenergy.2022.120241
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    1. Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
    2. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.
    3. Hang, Xinyu & Zhu, Xiaoxun & Gao, Xiaoxia & Wang, Yu & Liu, Longhu, 2024. "Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation," Renewable Energy, Elsevier, vol. 224(C).

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