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dc.contributor.author | Vives, Javier | es_ES |
dc.contributor.author | Palací, Juan | es_ES |
dc.date.accessioned | 2023-12-14T19:01:54Z | |
dc.date.available | 2023-12-14T19:01:54Z | |
dc.date.issued | 2022-10 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/200770 | |
dc.description.abstract | [EN] In this work, we combine some of the most relevant artificial intelligence (AI) techniques with a range-resolved interferometry (RRI) instrument applied to the maintenance of a wind turbine. This method of automatic and autonomous learning can identify, monitor, and detect the electrical and mechanical components of wind turbines to predict, detect, and anticipate their degeneration. A scanner laser is used to detect vibrations in two different failure states. Following each working cycle, RRI in-process measurements agree with in-process hand measurements of on-machine micrometers, as well as laser scanning in-process measurements. As a result, the proposed method should be very useful for supervising and diagnosing wind turbine faults in harsh environments. In addition, it will be able to perform in-process measurements at low costs. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Interferometry | es_ES |
dc.subject | Fault diagnosis | es_ES |
dc.subject | RRI | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Condition monitoring | es_ES |
dc.title | Artificial Intelligence and 3D Scanning Laser Combination for Supervision and Fault Diagnostics | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s22197649 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Vives, J.; Palací, J. (2022). Artificial Intelligence and 3D Scanning Laser Combination for Supervision and Fault Diagnostics. Sensors. 22(19):1-11. https://doi.org/10.3390/s22197649 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s22197649 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 11 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 22 | es_ES |
dc.description.issue | 19 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 36236753 | es_ES |
dc.identifier.pmcid | PMC9573344 | es_ES |
dc.relation.pasarela | S\478626 | es_ES |