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Damage identification of wind turbine blades with deep convolutional neural networks

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  • Guo, Jihong
  • Liu, Chao
  • Cao, Jinfeng
  • Jiang, Dongxiang

Abstract

Online early detection of surface damages on blades is critical for the safety of wind turbines, which could avoid catastrophic failures, minimize downtime, and enhance the reliability of the system. Monitoring the health status of blades is attracting more and more attention including on-site cameras and mobile cameras by drones and crawling robots. To deploy fast and efficient damage detection methods from image data, this work presents a hierarchical identification framework for wind turbine blades, which consists of a Haar-AdaBoost step for region proposal and a convolutional neural network (CNN) classifier for damage detection and fault diagnosis. Case studies are carried out on real data set collected from an eastern China wind farm. Results show that (i) the proposed framework can detect and identify the blade damages and outperforms other schemes include SVM and VGG16 models, (ii) sensitive analysis is conducted to validate the robustness of proposed method under limited data conditions, (iii) the proposed scheme is faster than one-step CNN method that directly classifying raw data.

Suggested Citation

  • Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    • Handle: RePEc:eee:renene:v:174:y:2021:i:c:p:122-133
    DOI: 10.1016/j.renene.2021.04.040
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    References listed on IDEAS

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    1. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
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    Cited by:

    1. 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).
    2. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. 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).
    4. 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.
    5. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    6. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.
    7. Artur Bejger & Jan Bohdan Drzewieniecki & Przemysław Bartoszko & Ewelina Frank, 2023. "The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades," Energies, MDPI, vol. 16(22), pages 1-17, November.

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