Structural Monitoring and Maintenance

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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)
  • Received : 2018.11.11
  • Accepted : 2018.11.28
  • Published : 2018.12.25
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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.

Keywords

Acknowledgement

Supported by : Pukyong National University

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