Algorithm for Damage Detection in Wind Turbine Blades using a Hybrid Dense Sensor Network with Feature Level Data Fusion

@inproceedings{DowneyAlgorithmFD,
  title={Algorithm for Damage Detection in Wind Turbine Blades using a Hybrid Dense Sensor Network with Feature Level Data Fusion},
  author={Austin Downey and Filippo Ubertini and Simon Laflamme}
}
Damage detection in wind turbine blades requires the ability to distinguish local faults over the blades global area. The implementation of dense sensor networks provides a solution to this local-global monitoring challenge. Here the authors propose a hybrid dense sensor network consisting of capacitive-based thin-film sensors for monitoring the additive strain over large areas and fiber Bragg grating sensors for enforcing boundary conditions. This hybrid dense sensor network is leveraged to… CONTINUE READING
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Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 33 references

Structural health monitoring techniques for wind turbine blades

  • A. Ghoshal, M. J. Sundaresan, M. J. Schulz, P. F. Pai
  • Journal of Wind Engineering and Industrial Aero-
  • 2000
Highly Influential
4 Excerpts

Experimental study of thin film sensor networks for wind turbine blade damage

  • D. E. Chimenti, L. J. Bond
  • Review of Progress in Quantitative Nondestructive…
  • 2016

Fully integrated patterned carbon nanotube strain sensors on flexible sensing skin substrates for structural health monitoring

  • A. R. Burton, M. Kurata, H. Nishino, J. P. Lynch
  • 2016
1 Excerpt

Technical , economic and uncertainty 730 modelling of a wind farm project

  • S. Afanasyeva, J. Saari, M. Kalkofen, J. Partanen, O. Pyrhönen
  • Energy Conversion and Management
  • 2016
1 Excerpt

Technical, economic and uncertainty

  • O. Pyrhönen
  • 2016

Dam - 785 age detection and localization from dense network of strain sensors

  • S. Laflamme, M. Kollosche, J. J. Connor, G. Kofod
  • 2015
1 Excerpt

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