IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i9p2197-d1642699.html
   My bibliography  Save this article

Automated Detection Method for Bolt Detachment of Wind Turbines in Low-Light Scenarios

Author

Listed:
  • Jiayi Deng

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China)

  • Yong Yao

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China)

  • Mumin Rao

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China)

  • Yi Yang

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China)

  • Chunkun Luo

    (Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
    National Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha 410082, China)

  • Zhenyan Li

    (Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
    National Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha 410082, China)

  • Xugang Hua

    (Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
    National Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha 410082, China)

  • Bei Chen

    (Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
    National Key Laboratory of Bridge Safety and Resilience, Hunan University, Changsha 410082, China)

Abstract

Tower bolts play a crucial role as connecting components in wind turbines and are of great interest for health monitoring systems. Non-contact monitoring techniques offer superior efficiency, convenience, and intelligence compared to contact-based methods. However, the precision and robustness of the non-contact monitoring process are significantly impacted by suboptimal lighting conditions within the wind turbine tower. To address this problem, this article proposes an automated detection method for the bolt detachment of wind turbines in low-light scenarios. The approach leverages the deep convolutional generative adversarial network (DCGAN) to expand and augment the small-sample bolt dataset. Transfer learning is then applied to train the Zero-DCE++ low-light enhancement model and the bolt defect detection model, with the experimental verification of the proposed method’s effectiveness. The results reveal that the deep convolutional generative adversarial network can generate realistic bolt images, thereby improving the quantity and quality of the dataset. Additionally, the Zero-DCE++ light enhancement model significantly increases the mean brightness of low-light images, resulting in a decrease in the error rate of defect detection from 31.08% to 2.36%. In addition, the model’s detection performance is affected by shooting angles and distances. Maintaining a shooting distance within 1.6 m and a shooting angle within 20° improves the reliability of the detection results.

Suggested Citation

  • Jiayi Deng & Yong Yao & Mumin Rao & Yi Yang & Chunkun Luo & Zhenyan Li & Xugang Hua & Bei Chen, 2025. "Automated Detection Method for Bolt Detachment of Wind Turbines in Low-Light Scenarios," Energies, MDPI, vol. 18(9), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2197-:d:1642699
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/9/2197/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/9/2197/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    2. Guan, Yang & Meng, Zong & Gu, Fengshou & Cao, Yanling & Li, Dongqin & Miao, Xiaopeng & Ball, Andrew D., 2025. "Fault diagnosis of wind turbine structures with a triaxial vibration dual-branch feature fusion network," Reliability Engineering and System Safety, Elsevier, vol. 256(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. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    2. Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    3. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    4. Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2024. "Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)," Applied Energy, Elsevier, vol. 360(C).
    5. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    6. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    7. Turowski, M. & Heidrich, B. & Weingärtner, L. & Springer, L. & Phipps, K. & Schäfer, B. & Mikut, R. & Hagenmeyer, V., 2024. "Generating synthetic energy time series: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).

    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:gam:jeners:v:18:y:2025:i:9:p:2197-:d:1642699. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.