DOI QR코드

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Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment

  • Choi, Mijin (Department of Aerospace Engineering & LANL-CBNU Engineering Institute, Chunbuk National University) ;
  • Jung, Hwee Kwon (School of Mechanical Engineering, Chonnam National University) ;
  • Taylor, Stuart G. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Farinholt, Kevin M. (The Engineering Institute, Los Alamos National Laboratory) ;
  • Lee, Jung-Ryul (Department of Aerospace Engineering, KAIST) ;
  • Park, Gyuhae (School of Mechanical Engineering, Chonnam National University)
  • 투고 : 2015.10.12
  • 심사 : 2016.03.10
  • 발행 : 2016.04.30

초록

This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.

키워드

참고문헌

  1. U. I. K Galappaththi, A. M. De Silva, M. Draskovic and M. Macdonald, "Strategic quality control measures to reduce defects in composite wind turbine blades," Proceedings of the International Conference on Renewable Energies and Power Quality, Bilbao (Spain) (2013)
  2. H. Sohn, C. R. Farra, N. Hunter and K. Worden, "Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surfaceeffect fast patrol boat," LA-13761-MS, The Enginerring Institute, Los Alamos National Laboratory, NM (US) (2001)
  3. E. Figueiredo, G. Park, J. Figueriras, C. Farrar and K. Worden, "Structural health monitoring algorithm comparisons using standard data sets," No. LA-14393, The Engineering Institute, Los Alamos National Laboratory (2009)
  4. S. G. Taylor, K. Farinholt, M. Choi, H. Jeong, J. Jang, G. Park, J. R. Lee and M. D. Todd, "Incipient crack detection in a composite wind turbine rotor blade," Journal of Intelligent Material Systems and Structures, Vol. 23, No. 5, pp. 613-620 (2013)
  5. N. Dervilis, M. Choi, S. G. Taylor, J. R. Barthorpe, G. Park, C. R. Farrar and K. Worden "On damage diagnosis for a wind turbine blade using pattern recognition," Journal of Sound and Vibration, Vol. 333, pp. 1833-1850 (2014) https://doi.org/10.1016/j.jsv.2013.11.015
  6. N. Dervilis, M. Choi, I. Antoniadou, K. Farinholt, S. G. Taylor and R. Barthorpe, "Novelty detection applied to vibration data from a CX100 wind turbine blade under fatigue loading," Journal of Physics Conference Series 382(1): 012047 (2012) https://doi.org/10.1088/1742-6596/382/1/012047
  7. H. Sun, Y. Zi and Z. He, "Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold," Applied Acoustics, Vol. 77, pp. 122-129 (2014) https://doi.org/10.1016/j.apacoust.2013.04.016
  8. J. Sierra-Perez, M. A. Torres-Arredondo and A. Guemes, "Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison," Composite Structures, Vol. 135, pp. 156-166 (2016) https://doi.org/10.1016/j.compstruct.2015.08.137
  9. F. P. G. Marquez, A. M. Tobias, J. M. P. Perez and M. Papaelias, "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Vol. 46, pp. 169-178 (2012) https://doi.org/10.1016/j.renene.2012.03.003
  10. A. Jungert, "Damage detection in wind turbine blades using two different acoustic techniques," The NDT Database & Journal (NDT) (2008)
  11. W. Y. Liu, B. P. Tang, J. G. Han, X. N. Lu, N. N. Hu and Z. Z. He, "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: a review," Renewable and Sustainable Energy Reviews, Vol. 44, pp. 466-472 (2015) https://doi.org/10.1016/j.rser.2014.12.005
  12. K. E. Johnson and P. A. Fleming, "Development, implementation, and testing of fault detection strategies on the national wind technology center's controls advanced research turbines," Mechatronices, Vol. 21(4), pp. 728-736 (2011) https://doi.org/10.1016/j.mechatronics.2010.11.010
  13. S. G. Taylor, G. Park, K. F. Farinholt, and M. D. Todd, "Fatigue crack detection performance comparison in a composite wind turbine rotor blade," International Journal of Structural Health Monitoring, Vol. 12, No. 3, pp. 252-262 (2013) https://doi.org/10.1177/1475921712471414
  14. K. M. Farinhot, S. G. Taylor, G. Park and Cutt M. Ammerman, "Full-scale fatigue tests of CX-100 wind turbine blades. Part I: testing," SPIE Smart Structures+ Nondestructive Evaluation and Health Monitoring, International Society for Optics and Photonics, p. 83430P-83430P-8 (2012)
  15. E. Figueiredo, G. Park, K. M. Farinholt, C. R. Farrar, and J. R. Lee, "Use of time-series predictive models for piezoelectric active-sensing in structural health monitoring applications," ASME Journal of Vibration and Acoustics, Vol. 134, No. 4, p. 041014 (2012) https://doi.org/10.1115/1.4006410