DOI QR코드

DOI QR Code

Vibration-based damage detection in wind turbine towers using artificial neural networks

  • Nguyen, Cong-Uy (Department of Ocean Engineering, Pukyong National University) ;
  • Huynh, Thanh-Canh (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National University)
  • 투고 : 2018.11.11
  • 심사 : 2018.11.28
  • 발행 : 2018.12.25

초록

In this paper, damage assessment in wind-turbine towers using vibration-based artificial neural networks (ANNs) is numerically investigated. At first, a vibration-based ANNs algorithm is designed for damage detection in a wind turbine tower. The ANNs architecture consists of an input, an output, and hidden layers. Modal parameters of the wind turbine tower such as mode shapes and frequencies are utilized as the input and the output layer composes of element stiffness indices. Next, the finite element model of a real wind-turbine tower is established as the test structure. The natural frequencies and mode shapes of the test structure are computed under various damage cases of single and multiple damages to generate training patterns. Finally, the ANNs are trained using the generated training patterns and employed to detect damaged elements and severities in the test structure.

키워드

과제정보

연구 과제 주관 기관 : Pukyong National University

참고문헌

  1. Benedetti, M., Fontanari, V. and Zonta, D. (2011), "Structural health monitoring of wind towers: remote damage detection using strain sensors", Smart Materi. Struct., 20, 1-13.
  2. Farrar, C.R. (1997), "System identification from ambient vibration measurements on a bridge", J. Sound Vib., 205(1), 1-18 https://doi.org/10.1006/jsvi.1997.0977
  3. Huynh, T.C., Park, J.H. and Kim, J.T. (2016), "Structural identification of cable-stayed bridge under back-to-back typhoons by wireless vibration monitoring", Measurement, 88, 385-401. https://doi.org/10.1016/j.measurement.2016.03.032
  4. Kim, J.T. and Stubbs, N. (1995), "Model uncertainty and damage detection accuracy in plate-girder bridges", J. Struct. Eng., 121(10), 1409-1417 https://doi.org/10.1061/(ASCE)0733-9445(1995)121:10(1409)
  5. Kim, J.T., Huynh, T.C. and Lee, S.Y. (2014), "Wireless structural health monitoring of stay cables under two consecutive typhoons", Struct. Monit. Maint., 1(1), 47-67. https://doi.org/10.12989/SMM.2014.1.1.047
  6. Kim, J.T., Ryu, Y.S., Cho H.M. and Stubbs, N. (2003), "Damage identification in beam-type structures: Frequency-based method vs mode-shape-based method", Eng. Struct., 25, 57-67. https://doi.org/10.1016/S0141-0296(02)00118-9
  7. Lee, J.J., Lee, J.W., Yia, J.H, Yun, C.B. and Jung, H.Y. (2004), "Neural networks-based damage detection for bridges considering errors in baseline finite element models", J. Sound Vib., 280, 555-578
  8. Li, H.N., Li, D.S., Ren, L., Yi, T.H., Jia, Z.G. and LI, K.P. (2016), "Structural health monitoring of innovative civil engineering structures in Mainland China", Struct. Monit. Maint., 3(1), 1-32. https://doi.org/10.12989/SMM.2016.3.1.001
  9. Li, H.N., Yi, T.H., Ren, L., Li, D.S. and Huo, L.S. (2014), "Review on innovations and applications in structural health monitoring for infrastructures", Struct. Monit. Maint., 1(1), 1-45. https://doi.org/10.12989/SMM.2014.1.1.001
  10. Li. Z.X. and Yang. X.M. (2008), "Damage identification for beams using ANN based on statistical property of structural response", Comput. Struct., 86(1), 64-71. https://doi.org/10.1016/j.compstruc.2007.05.034
  11. Martinez-Luengo, M., Lolios, A. and Wang, L. (2016), "Structural health monitoring of offshore wind turbines: A review through the statistical pattern recognition paradigm", Renew. Sust. Energ. Rev., 64, 91-105. https://doi.org/10.1016/j.rser.2016.05.085
  12. Nguyen, C.U., Huynh, T.C., Dang, N.L. and Kim, J.T. (2017), "Vibration-based damage alarming criteria for wind turbine towers", Struct. Monit. Maint., 4(3), 221-236. https://doi.org/10.12989/SMM.2017.4.3.221
  13. Nguyen, T.C., Huynh, T.C. and Kim, J.T. (2015), "Numerical evaluation for vibration-based damage detection in wind turbine tower structure", Wind Struct., 21(6), 657-675. https://doi.org/10.12989/WAS.2015.21.6.657
  14. Nguyen, T.C., Huynh, T.C., Yi, J.H. and Kim, J.T. (2017), "Hybrid bolt-loosening detection in wind turbine tower structures by vibration and impedance responses", Wind Struct., 24(4), 385-403. https://doi.org/10.12989/WAS.2017.24.4.385
  15. Ni, Y.Q., Zhou, X.T., Ko, J.M. and Wang, B.S. (2002), "Vibration-based damage localization in Ting Kau Bridge using probabilistic neural network", Adv. Struct. Dynamics. 2, 1069-1076.
  16. Pandey, A.K. and Biswas, M. (1994), "Damage detection in structures using changes in flexibility", J. Sound Vib., 169(1), 3-17. https://doi.org/10.1006/jsvi.1994.1002
  17. Park, J. (2015), Annual Report on Wind Energy Industry of Korea 2015, Korea Wind Energy Industry Association
  18. Park, J.H., Huynh, T.C., Choi, S.H. and Kim, J.T. (2015), "Vision-based technique for bolt-loosening detection in wind turbine tower", Wind Struct., 21(6), 709-726. https://doi.org/10.12989/WAS.2015.21.6.709
  19. Park, J.H., Kim, J.T., Hong, D.S., Ho, D.D. and Yi, J.H. (2009), "Sequential damage detection approaches for beams using time-modal features and artificial neural networks", J. Sound Vib., 323(1-2), 451-474. https://doi.org/10.1016/j.jsv.2008.12.023
  20. Qu, C.X., Yi, T.H., Yang, X.M. and Li, H.N. (2017), "Spurious mode distinguish by eigensystem realization algorithm with improved stabilization diagram", Struct. Eng. Mech., 63(6), 743-750. https://doi.org/10.12989/SEM.2017.63.6.743
  21. Shu, J., Zhang, Z., Gonzalez, I. and Karoumi, R. (2012), "The application of a damage detection method using Artificial Neural Network and train-induced vibrations on a simplified railway bridge model", Eng. Struct., 52, 408-421.
  22. Sutar, M.K., Sarojrani. P. and Jayadev, R. (2015), "Neural based controller for smart detection of crack in cracked cantilever beam", Proceeding of Materials Today, 2, 2648-2653. https://doi.org/10.1016/j.matpr.2015.07.225
  23. Vandiver, J.K. (1977), "Detection of structural failure on fixed platforms by measurement of dynamic response", J. Petrol. Technol., 29(3), 305-310. https://doi.org/10.2118/5679-PA
  24. Yi, J.H. and Yun, C.B. (2004), "Comparative study on modal identification methods using output-only information", Struct. Eng. Mech., 17(3-4), 445-466. https://doi.org/10.12989/sem.2004.17.3_4.445

피인용 문헌

  1. Vibration-Based Damage Assessment in Gravity-Based Wind Turbine Tower under Various Waves vol.2019, pp.None, 2018, https://doi.org/10.1155/2019/1406861
  2. Feasibility for Damage Identification in Offshore Wind Jacket Structures through Monitoring of Global Structural Dynamics vol.13, pp.21, 2018, https://doi.org/10.3390/en13215791
  3. Big data platform for health monitoring systems of multiple bridges vol.7, pp.4, 2020, https://doi.org/10.12989/smm.2020.7.4.345