Smart Structures and Systems

  • 제14권3호
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  • Pages.419-444
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Novel approach for early damage detection on rotor blades of wind energy converters

  • Zerbst, Stephan (Institute of Structural Analysis, Leibniz Universitat Hannover) ;
  • Tsiapoki, Stavroula (Institute of Structural Analysis, Leibniz Universitat Hannover) ;
  • Rolfes, Raimund (Institute of Structural Analysis, Leibniz Universitat Hannover)
  • 투고 : 2012.02.09
  • 심사 : 2014.03.04
  • 발행 : 2014.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Within this paper a new approach for early damage detection in rotor blades of wind energy converters is presented, which is shown to have a more sensitive reaction to damage than eigenfrequency-based methods. The new approach is based on the extension of Gasch's proportionality method, according to which maximum oscillation velocity and maximum stress are proportional by a factor, which describes the dynamic behavior of the structure. A change in the proportionality factor can be used as damage indicator. In addition, a novel deflection sensor was developed, which was specifically designed for use in wind turbine rotor blades. This deflection sensor was used during the experimental tests conducted for the measurement of the blade deflection. The method was applied on numerical models for different damage cases and damage extents. Additionally, the method and the sensing concept were applied on a real 50.8 m blade during a fatigue test in the edgewise direction. During the test, a damage of 1.5 m length was induced on the upper trailing edge bondline. Both the initial damage and the increase of its length were successfully detected by the decrease of the proportionality factor. This decrease coincided significantly with the decrease of the factor calculated from the numerical analyses.

키워드

참고문헌

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피인용 문헌

  1. Damage and ice detection on wind turbine rotor blades using a three-tier modular structural health monitoring framework 2017, https://doi.org/10.1177/1475921717732730