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Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser

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Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser

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Vives-Fuster, J.; Roses Albert, E.; Quiles, E.; Palací, J.; Fuster, T. (2022). Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser. Computational Intelligence and Neuroscience (Online). 2022:1-7. https://doi.org/10.1155/2022/2093086

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/202230

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Title: Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser
Author: Vives-Fuster, Javier Roses Albert, Eduardo Quiles, Emilio Palací, Juan Fuster, Teresa
Issued date:
Abstract:
[EN] With this research, we apply range-resolved interferometry (RRI) to the maintenance of wind turbines using some of the most relevant machine-learning (ML) techniques. The degeneration of electrical and mechanical ...[+]
Subjects: Machine Learning , Laser scanner , Monitoring , Interferometry , Fault Diagnosis
Copyrigths: Reconocimiento (by)
Source:
Computational Intelligence and Neuroscience (Online). (eissn: 1687-5273 )
DOI: 10.1155/2022/2093086
Publisher:
Hindawi Limited
Publisher version: https://doi.org/10.1155/2022/2093086
Type: Artículo

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