@ARTICLE{Xie_Wei-Guo_Non-destructive_2023,
 author={Xie, Wei-Guo and Zhou, Peng and Chen, Li-Yun and Gu, Guo-Qing and Wang, Yong-Qing and Chen, Yu-Tao},
 volume={vol. 30},
 number={No 4},
 journal={Metrology and Measurement Systems},
 pages={839-850},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={Looseness of high-strength wind turbine bolts is one of the main types of mechanical failure that threaten the quality and safety of wind turbines, and how to non-destructively detect bolt loosening is essential to accurate assessment of operational reliability of wind turbine structures. Therefore, to address the issue of looseness detection of high-strength wind turbine bolts, this paper proposes a non-destructive detection method based on digital image correlation (DIC). Firstly, the mathematical relationships between the inplane displacement component of the bolt’s nut surface, the bolt’s preload force loss and the bolt loosening angle are both deduced theoretically. Then, experimental measurements are respectively conducted with DIC with different small bolt loosening angles. The results show that the bolt loosening angle detection method based on DIC has a detection accuracy of over 95%, and the bolt’s preload force loss evaluated by the deduced relationship has a good agreement with the empirical value. Therefore, the proposed DIC-based bolt loosening angle detection method can meet the requirements of engineering inspection, and can achieve quantitative assessment of preload forces loss of wind turbine bolt.},
 type={Article},
 title={Non-destructive detection of high-strength wind turbine bolt looseness using digital image correlation},
 URL={http://www.czasopisma.pan.pl/Content/130317/art15_int.pdf},
 doi={10.24425/mms.2023.147948},
 keywords={Bolt looseness detection, digital image correlation, loosening angle, preload force loss},
}