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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
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    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).
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