Gaining insight in wind turbine drivetrain dynamics by means of automatic operational modal analysis combined with machine learning algorithms

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 con...

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Bibliographic Details
Published in:Volume 10: Ocean Renewable Energy
Main Authors: Gioia, Nicoletta, Daems, P. J., Peeters, C., Guillaume, P., Helsen, J., Medico, Roberto, Deschrijver, Dirk, Dhaene, Tom
Format: Conference Object
Language:English
Published: ASME 2019
Subjects:
Technology and Engineering
Arctic
Online Access:https://biblio.ugent.be/publication/8644551
http://hdl.handle.net/1854/LU-8644551
https://doi.org/10.1115/omae2019-96731
https://biblio.ugent.be/publication/8644551/file/8667176
Description
Summary: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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