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Structural Monitoring and Maintenance Volume 5, Number 4, December 2018, pages 507-519 DOI: http://dx.doi.org/10.12989/smm.2018.5.4.507 |
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Vibration-based damage detection in wind turbine towers using artificial neural networks |
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Cong-Uy Nguyen, Thanh-Canh Huynh and Jeong-Tae Kim
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| 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. | ||
| Key Words | ||
| wind turbine; vibration; frequency; mode shape; ANN; damage detection; finite element model | ||
| Address | ||
| Cong-Uy Nguyen, Thanh-Canh Huynh and Jeong-Tae Kim: Department of Ocean Engineering, Pukyong National University, 599-1 Daeyon-3dong, Nam-gu, Busan 608-737, Republic of Korea | ||