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Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier

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  • Yang, Xiyun
  • Zhang, Yanfeng
  • Lv, Wei
  • Wang, Dong

Abstract

An image recognition model based on a deep learning network is proposed for the automatic extraction of image features and the accurate and efficient detection of wind turbine blade damage. The Otsu threshold segmentation method is used to segment the blade image to eliminate the influence of the image background on the detection task. In order to improve the recognition performance of the proposed deep learning model, transfer learning and an ensemble learning classifier are used in a convolutional neural network model. Transfer learning is used to enhance the ability of the proposed model to extract abstract features and accelerate the convergence efficiency, whereas the random forest-based ensemble learning classifier is used to improve the accuracy of detecting the blade defects. The performance of the proposed model is verified by using unmanned aerial vehicle (UAV) images of the wind turbine blades. The proposed model provided better performance than the support vector machine (SVM) method, the basic deep learning model and the deep learning model combined with the ensemble learning approach.

Suggested Citation

  • Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:386-397
    DOI: 10.1016/j.renene.2020.08.125
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    Cited by:

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    6. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
    7. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    8. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.
    9. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    10. Yang, Cong & Liu, Xun & Zhou, Hua & Ke, Yan & See, John, 2023. "Towards accurate image stitching for drone-based wind turbine blade inspection," Renewable Energy, Elsevier, vol. 203(C), pages 267-279.
    11. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    12. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    13. Xu, Zifei & Bashir, Musa & Yang, Yang & Wang, Xinyu & Wang, Jin & Ekere, Nduka & Li, Chun, 2022. "Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism," Renewable Energy, Elsevier, vol. 199(C), pages 21-34.
    14. Liu, Y. & Hajj, M. & Bao, Y., 2022. "Review of robot-based damage assessment for offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    15. Jian Wang & Jin Gao & Yong Zhang & Hongmei Cui, 2024. "Analysis of the Sand Erosion Effect and Wear Mechanism of Wind Turbine Blade Coating," Energies, MDPI, vol. 17(2), pages 1-18, January.

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