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Detailed knowledge about the modal model is essential to enhance the NVH behavior of (rotating) machines. To have more realistic insight in the modal behavior of the machines, observation of modal parameters must be extended to a significant amount of time, in which all the significant operating conditions of the turbine can be investigated, together with the transition events from one operating condition to another. To allow the processing of a large amount of data, automated OMA techniques are used: once frequency and damping values can be characterized for the important resonances, it becomes possible to gain insights in their changes. This paper will focus on processing experimental data of an offshore wind turbine gearbox and investigate the changes in resonance frequency and damping over time.

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MLA
Gioia, Nicoletta, et al. “Gaining Insight in Wind Turbine Drivetrain Dynamics by Means of Automatic Operational Modal Analysis Combined with Machine Learning Algorithms.” PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10, ASME, 2019, doi:10.1115/omae2019-96731.
APA
Gioia, N., Daems, P. J., Peeters, C., Guillaume, P., Helsen, J., Medico, R., … Dhaene, T. (2019). Gaining insight in wind turbine drivetrain dynamics by means of automatic operational modal analysis combined with machine learning algorithms. In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10. New York: ASME. https://doi.org/10.1115/omae2019-96731
Chicago author-date
Gioia, Nicoletta, P. J. Daems, C. Peeters, P. Guillaume, J. Helsen, Roberto Medico, Dirk Deschrijver, and Tom Dhaene. 2019. “Gaining Insight in Wind Turbine Drivetrain Dynamics by Means of Automatic Operational Modal Analysis Combined with Machine Learning Algorithms.” In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10. New York: ASME. https://doi.org/10.1115/omae2019-96731.
Chicago author-date (all authors)
Gioia, Nicoletta, P. J. Daems, C. Peeters, P. Guillaume, J. Helsen, Roberto Medico, Dirk Deschrijver, and Tom Dhaene. 2019. “Gaining Insight in Wind Turbine Drivetrain Dynamics by Means of Automatic Operational Modal Analysis Combined with Machine Learning Algorithms.” In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10. New York: ASME. doi:10.1115/omae2019-96731.
Vancouver
1.
Gioia N, Daems PJ, Peeters C, Guillaume P, Helsen J, Medico R, et al. Gaining insight in wind turbine drivetrain dynamics by means of automatic operational modal analysis combined with machine learning algorithms. In: PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10. New York: ASME; 2019.
IEEE
[1]
N. Gioia et al., “Gaining insight in wind turbine drivetrain dynamics by means of automatic operational modal analysis combined with machine learning algorithms,” in PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10, Univ Strathclyde, Glasgow, Scotland, 2019.
@inproceedings{8644551,
  abstract     = {Detailed knowledge about the modal model is essential to enhance the NVH behavior of (rotating) machines. To have more realistic insight in the modal behavior of the machines, observation of modal parameters must be extended to a significant amount of time, in which all the significant operating conditions of the turbine can be investigated, together with the transition events from one operating condition to another. To allow the processing of a large amount of data, automated OMA techniques are used: once frequency and damping values can be characterized for the important resonances, it becomes possible to gain insights in their changes. This paper will focus on processing experimental data of an offshore wind turbine gearbox and investigate the changes in resonance frequency and damping over time.},
  articleno    = {V010T09A017},
  author       = {Gioia, Nicoletta and Daems, P. J. and Peeters, C. and Guillaume, P. and Helsen, J. and Medico, Roberto and Deschrijver, Dirk and Dhaene, Tom},
  booktitle    = {PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2019, VOL 10},
  isbn         = {9780791858899},
  issn         = {2153-4772},
  language     = {eng},
  location     = {Univ Strathclyde, Glasgow, Scotland},
  pages        = {7},
  publisher    = {ASME},
  title        = {Gaining insight in wind turbine drivetrain dynamics by means of automatic operational modal analysis combined with machine learning algorithms},
  url          = {http://dx.doi.org/10.1115/omae2019-96731},
  year         = {2019},
}

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