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

Jiayi Deng, Yong Yao, Mumin Rao, Yi Yang, Chunkun Luo (), Zhenyan Li, Xugang Hua and Bei Chen ()
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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
Zhenyan Li: Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
Xugang Hua: Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China
Bei Chen: Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China

Energies, 2025, vol. 18, issue 9, 1-25

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.

Keywords: wind turbine; bolt detachment; low-light scenario; deep learning; automated detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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