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Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals
Abstract
It is important from the economic point of view to detect damage early in the wind turbines before failures occur. For this purpose, a monitoring device was built that analyzes both acoustic signals acquired from the built-in non-contact acoustic intensity probe, as well as from the accelerometers, mounted on the internal devices in the nacelle. The signals collected in this way are used for long-term training of the autoencoder neural network. The appropriately trained network automatically detects deviations, signaling them to technical service. The applied methods of analysis of vibroacoustic signals and neural network training are the subject of the presented paper. In addition, the motion magnification of video is used for extracting information on vibrations of the whole wind turbine construction. Finally, spectral analysis is applied for detecting unnatural components presence meaning defects in both: visual and vibroacoustic representations. The process of reduction and construction of a wind turbine model is discussed with a particular emphasis on application to perform extensive tests of the developed methods and algorithms. [The research was subsidized by the Polish National Centre for Research and Development within the project “STEO—System for Technical and Economic Optimization of Distributed Renewable Energy Sources”, No. POIR.01.02.00-00-0357/16.]
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- Publication version
- Accepted or Published Version
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- Copyright (2019 Acoustical Society of America)
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Journal of the Acoustical Society of America
no. 146,
pages 2952 - 2952,
ISSN: 0001-4966 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Czyżewski A.: Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals// Journal of the Acoustical Society of America -Vol. 146,iss. 4 (2019), s.2952-2952
- DOI:
- Digital Object Identifier (open in new tab) 10.1121/1.5137251
- Verified by:
- Gdańsk University of Technology
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