Browse > Article
http://dx.doi.org/10.12989/smm.2018.5.4.507

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)
Publication Information
Structural Monitoring and Maintenance / v.5, no.4, 2018 , pp. 507-519 More about this Journal
Abstract
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.
Keywords
wind turbine; vibration; frequency; mode shape; ANN; damage detection; finite element model;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
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   DOI
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.   DOI
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   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
13 Pandey, A.K. and Biswas, M. (1994), "Damage detection in structures using changes in flexibility", J. Sound Vib., 169(1), 3-17.   DOI
14 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.   DOI
15 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.   DOI
16 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.
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.   DOI
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.   DOI
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.   DOI
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 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.   DOI
23 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.   DOI
24 Vandiver, J.K. (1977), "Detection of structural failure on fixed platforms by measurement of dynamic response", J. Petrol. Technol., 29(3), 305-310.   DOI