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Non-destructive detection of high-strength wind turbine bolt looseness using digital image correlation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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 in-plane 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.
Rocznik
Strony
839--850
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr., wzory
Twórcy
autor
  • Yancheng Institute of Supervision & Inspection on Product Quality, Yancheng 224056, China
autor
  • Yancheng Institute of Supervision & Inspection on Product Quality, Yancheng 224056, China
autor
  • Yancheng Institute of Supervision & Inspection on Product Quality, Yancheng 224056, China
autor
  • School of Civil Engineering, Yancheng Institute of Technology, Yancheng 224051, China
  • School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
autor
  • School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Bibliografia
  • [1] Lalonde, E. R., Dai, K. S., Lu, W. S., & Bitsuamlak, G. (2019). Wind turbine testing methods and application of hybrid testing: A review. Wind and Structures, 29(3), 195-207. https://doi.org/10.12989/was.2019.29.3.195
  • [2] Brouwer, S. R., Al-Jibouri, S. H., Cardenas, I. C., & Halman, J. I. (2018). Towards analysing risks to public safety from wind turbines. Reliability Engineering & System Safety, 180, 77-87. https://doi.org/10.1016/j.ress.2018.07.010
  • [3] Rincon-Casado, A., Julia-Lerma, J. M., Garcia-Vallejo, D., & Dominguez, J. (2022). Experimental estimation of the residual fatigue life on in-service wind turbine bolts. Engineering Failure Analysis, 141, 106658. https://doi.org/10.1016/j.engfailanal.2022.106658
  • [4] Li, S. Z., Li, H., Zhou, X. H., Wang, Y. H., Li, X. H., Gao, D., & Zhu, R. H. (2023). Damage detection of flange bolts in wind turbine towers using dynamic strain responses. Journal of Civil Structural Health Monitoring, 13(1), 67-81. https://doi.org/10.1007/s13349-022-00622-z
  • [5] Civera, M., & Surace, C. (2022). Non-destructive techniques for the condition and structural health monitoring of wind turbines: A literature review of the last 20 years. Sensors, 22(4), 1627. https://doi.org/10.3390/s22041627
  • [6] Chen, J., He, C. F., Lyu, Y., Zhang, Y., Xie, L. Y., & Wu, L. (2020). Ultrasonic inspection of the surface crack for the main shaft of a wind turbine from the end face. NDT & E International, 114, 102283. https://doi.org/10.1016/j.ndteint.2020.102283
  • [7] Yue, Y. C., Tian, J. J., Bai, Y. T., Jia, K., He, J., Luo, D., & Chen, T. B. (2021). Applicability analysis of inspection and monitoring technologies in wind turbine towers. Shock Vibration, 2021, 5548727. https://doi.org/10.1155/2021/5548727
  • [8] Pezzani, C. M., Bossio, J. M., Castellino, A. M., Bossio, G. R., & De Angelo, C. H. (2014). Bearing fault detection in wind turbines with permanent magnet synchronous machines. IEEE Latin America Transactions, 12(7), 1199-1205. https://doi.org/10.1109/TLA.2014.6948853
  • [9] Sampath, U., Kim, H., Kim, D. G., Kim, Y. C., & Song, M. (2015). In-situ cure monitoring of wind turbine blades by using fiber Bragg grating sensors and Fresnel reflection measurement. Sensors, 15(8), 18229-18238. https://doi.org/10.3390/s150818229
  • [10] Kim, S. T., Yoon, H., Park, Y. H., Jin, S. S., Shin, S., & Yonn, S. M. (2011). Smart sensing of PSC girders using a PC strand with a built-in optical fiber sensor. Applied Sciences, 11(1), 359. https://doi.org/10.3390/app11010359
  • [11] Kim, D., Han, S., Kim, T., Kim, C., Lee, D., Kang, D., & Koh, J. S. (2021). Design of a sensitive balloon sensor for safe human-robot interaction. Sensors, 21(6), 2163. https://doi.org/10.3390/s21062163
  • [12] Chen, C. X., Ma, T. H., Jin, H., Wu, Y. Y., Hou, Z. W., & Li, F. (2020). Torque and rotational speed sensor based on resistance and capacitive grating for rotational shaft of mechanical systems. Mechanical Systems and Signal Processing, 142, 106737. https://doi.org/10.1016/j.ymssp.2020.106737
  • [13] Park, J. H., Huynh, T. C., Choi, S. H., & Kim, J. T. (2015). Vision-based technique for bolt-loosening detection in wind turbine tower. Wind and Structures, 21(6), 709-726. https://doi.org/10.12989/was.2015.21.6.709
  • [14] Nguyen, C. U., Lee, S. Y., Huynh, T. C., Kim, H. T., & Kim, J. T. (2019). Vibration characteristics of offshore wind turbine tower with gravity-based foundation under wave excitation. Smart Structures and Systems, 23, 405-420. https://doi.org/10.12989/sss.2019.23.5.405
  • [15] Cha, Y. J., You, K., & Choi, W. (2016). Vision-based detection of loosened bolts using the Hough transform and support vector machines. Automation in Construction, 71, 181-188. https://doi.org/10.1016/j.autcon.2016.06.008
  • [16] Huynh, T. C. (2021). Vision-based autonomous bolt-looseness detection method for splice connections: Design, lab-scale evaluation, and field application. Automation in Constructio, 124, 103591. https://doi.org/10.1016/j.autcon.2021.103591
  • [17] Yang, X. Y., Gao, Y. Q., Feng, C., Zhang, Y., & Wang, W. (2022). Deep learning-based bolt loosening detection for wind turbine towers. Structural Control and Health Monitoring, 29(6), e2943. https://doi.org/10.1002/stc.2943
  • [18] Pan, B., Zhang, X. Y., Lv, Y., L. P., & Yu, T. (2022). Automatic optimal camera exposure time control for digital image correlation. Measurement Science and Technology, 33(10), 105205. https://doi.org/10.1088/1361-6501/ac750e
  • [19] Han, S. H., He, Y. M., Lei, J., Tian, Y., Hu, Y. Y., Xie, Y. Y., & Yang, Y. B. (2023). Analysis of displacement fields with large deformations using an improved spectral digital image correlation method. Optik, 283, 170901. https://doi.org/10.1016/j.ijleo.2023.170901
  • [20] Wang, L., Liu, G. Y., Deng, Y. W., Sun, W. Z., Ma, Q. W., & Ma, S. P. (2023). Investigation on out-of-plane displacement measurements of thin films via a mechanical constraint-based 3D-DIC technique. Optics Communications, 530, 129015. https://doi.org/10.1016/j.optcom.2022.129015
  • [21] Gu, G. Q., She, B., Xu, G. Z., & Xu, X. (2017). Non-uniform illumination correction based on the retinex theory in digital image correlation measurement method. Optica Applicata, 47(2), 199-208. https://doi.org/10.5277/oa170203
  • [22] Su, Y. (2023). An analytical study on the low-pass filtering effect of digital image correlation caused by under-matched shape functions. Optics and Lasers in Engineering, 168, 107679. https://doi.org/10.1016/j.optlaseng.2023.107679
  • [23] Khoo, S. W., Karuppanan, S., & Tan, C. S. (2016). A review of surface deformation and strain measurement using two-dimensional digital image correlation. Metrology and Measurement Systems, 23(3), 461-480. https://doi.org/10.1515/mms-2016-0028
  • [24] Kong, Z. Y., Jin, Y., Hong, S. Z., Liu, Q. W., Vu, Q. V., & Kim, S. E. (2022). Degradation behavior of the preload force of high-strength bolts after corrosion. Buildings, 12(12), 2122. https://doi.org/10.3390/buildings12122122
  • [25] Blaber, J., Adair, B., & Antoniou, A. (2015). Ncorr: Open-Source 2D Digital Image Correlation Matlab Software. Experimental Mechanics, 55(6), 1105-1122. https://doi.org/10.1007/s11340-015-0009-1
Uwagi
This work was supported by the Jiangsu Provincial Market Regulation Administration Science and Technology Project (No. KJ2022049).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-926145fb-d480-408b-a354-b6bb1ba53091
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