IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v328y2022ics0306261922014982.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922014982
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120241?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    2. Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
    3. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    4. 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.
    5. Wu, Xiaoni & Hu, Yu & Li, Ye & Yang, Jian & Duan, Lei & Wang, Tongguang & Adcock, Thomas & Jiang, Zhiyu & Gao, Zhen & Lin, Zhiliang & Borthwick, Alistair & Liao, Shijun, 2019. "Foundations of offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 379-393.
    6. Shah, Kamran Ali & Meng, Fantai & Li, Ye & Nagamune, Ryozo & Zhou, Yarong & Ren, Zhengru & Jiang, Zhiyu, 2021. "A synthesis of feasible control methods for floating offshore wind turbine system dynamics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    7. 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.
    8. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    9. Yang, Jinshui & Peng, Chaoyi & Xiao, Jiayu & Zeng, Jingcheng & Yuan, Yun, 2012. "Application of videometric technique to deformation measurement for large-scale composite wind turbine blade," Applied Energy, Elsevier, vol. 98(C), pages 292-300.
    10. Micallef, Daniel & Rezaeiha, Abdolrahim, 2021. "Floating offshore wind turbine aerodynamics: Trends and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    11. Mishnaevsky, Leon, 2019. "Repair of wind turbine blades: Review of methods and related computational mechanics problems," Renewable Energy, Elsevier, vol. 140(C), pages 828-839.
    12. Liu, Zepeng & Zhang, Long & Carrasco, Joaquin, 2020. "Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method," Renewable Energy, Elsevier, vol. 146(C), pages 99-110.
    13. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    14. Leon Mishnaevsky & Nicolai Frost-Jensen Johansen & Anthony Fraisse & Søren Fæster & Thomas Jensen & Brian Bendixen, 2022. "Technologies of Wind Turbine Blade Repair: Practical Comparison," Energies, MDPI, vol. 15(5), pages 1-17, February.
    15. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    16. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(C).
    2. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
    3. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    4. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    5. Majidi Nezhad, Meysam & Neshat, Mehdi & Piras, Giuseppe & Astiaso Garcia, Davide, 2022. "Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Grant, Elenya & Johnson, Kathryn & Damiani, Rick & Phadnis, Mandar & Pao, Lucy, 2023. "Buoyancy can ballast control for increased power generation of a floating offshore wind turbine with a light-weight semi-submersible platform," Applied Energy, Elsevier, vol. 330(PB).
    7. 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.
    8. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
    9. 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.
    10. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    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. Meng, Fantai & Sergiienko, Nataliia & Ding, Boyin & Zhou, Binzhen & Silva, Leandro Souza Pinheiro Da & Cazzolato, Benjamin & Li, Ye, 2023. "Co-located offshore wind–wave energy systems: Can motion suppression and reliable power generation be achieved simultaneously?," Applied Energy, Elsevier, vol. 331(C).
    13. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    14. 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.
    15. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    16. Truong, Hoai Vu Anh & Dang, Tri Dung & Vo, Cong Phat & Ahn, Kyoung Kwan, 2022. "Active control strategies for system enhancement and load mitigation of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    17. 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).
    18. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    19. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014982. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.