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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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dc.contributor.author Vives-Fuster, Javier es_ES
dc.contributor.author Roses Albert, Eduardo es_ES
dc.contributor.author Quiles, Emilio es_ES
dc.contributor.author Palací, Juan es_ES
dc.contributor.author Fuster, Teresa es_ES
dc.date.accessioned 2024-01-30T19:01:23Z
dc.date.available 2024-01-30T19:01:23Z
dc.date.issued 2022-12-26 es_ES
dc.identifier.uri http://hdl.handle.net/10251/202230
dc.description.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 components of wind turbines can be predicted, detected, and anticipated using this method of automatic and autonomous learning. The vibrations in two different failure states are detected with the help of a scanner laser. In-process measurements taken by RRI agree with manual measurements, laser scanning measurements, and in-process hand measurements made following each working cycle. Consequently, the proposed method will be very useful for monitoring and diagnosing faults in wind turbines. The system will also be able to perform low-cost in-process measurements. es_ES
dc.language Inglés es_ES
dc.publisher Hindawi Limited es_ES
dc.relation.ispartof Computational Intelligence and Neuroscience (Online) es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Machine Learning es_ES
dc.subject Laser scanner es_ES
dc.subject Monitoring es_ES
dc.subject Interferometry es_ES
dc.subject Fault Diagnosis es_ES
dc.title Vibration Analysis for Fault Detection of Wind Turbines by Combining Machine-Learning Techniques and 3D Scanning Laser es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2022/2093086 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2022/2093086 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 7 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2022 es_ES
dc.identifier.eissn 1687-5273 es_ES
dc.identifier.pmid 36601275 es_ES
dc.identifier.pmcid PMC9807294 es_ES
dc.relation.pasarela S\479800 es_ES


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