Application of deep neural networks for automatic rub detection in aero-derivative gas turbines
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Politécnica de Madrid |
| Repositorio: | Archivo Digital UPM |
| OAI Identifier: | oai:oa.upm.es:84433 |
| Acceso en línea: | https://oa.upm.es/84433/ |
| Access Level: | acceso abierto |
| Palabra clave: | Aeroderivative Gas Turbines Deep Neural Networks Fourier Transfor Fourier transform Rub Detection |
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Application of deep neural networks for automatic rub detection in aero-derivative gas turbinesSquicciarini, Antonio|||0000-0002-7870-6287Zarzo Altarejos, Alejandro|||0000-0002-1456-1744González Guillén, Carlos Eduardo|||0000-0002-1604-6244Muñoz Guijosa, Juan Manuel|||0000-0003-3738-7643Aeroderivative Gas TurbinesDeep Neural NetworksFourier TransforFourier transformRub Detection20242024-10-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articlehttps://oa.upm.es/84433/reponame:Archivo Digital UPMinstname:Universidad Politécnica de MadridInglésenopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:oa.upm.es:844332026-06-21T12:45:07Z |
| dc.title.none.fl_str_mv |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| title |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| spellingShingle |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines Squicciarini, Antonio|||0000-0002-7870-6287 Aeroderivative Gas Turbines Deep Neural Networks Fourier Transfor Fourier transform Rub Detection |
| title_short |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| title_full |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| title_fullStr |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| title_full_unstemmed |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| title_sort |
Application of deep neural networks for automatic rub detection in aero-derivative gas turbines |
| dc.creator.none.fl_str_mv |
Squicciarini, Antonio|||0000-0002-7870-6287 Zarzo Altarejos, Alejandro|||0000-0002-1456-1744 González Guillén, Carlos Eduardo|||0000-0002-1604-6244 Muñoz Guijosa, Juan Manuel|||0000-0003-3738-7643 |
| author |
Squicciarini, Antonio|||0000-0002-7870-6287 |
| author_facet |
Squicciarini, Antonio|||0000-0002-7870-6287 Zarzo Altarejos, Alejandro|||0000-0002-1456-1744 González Guillén, Carlos Eduardo|||0000-0002-1604-6244 Muñoz Guijosa, Juan Manuel|||0000-0003-3738-7643 |
| author_role |
author |
| author2 |
Zarzo Altarejos, Alejandro|||0000-0002-1456-1744 González Guillén, Carlos Eduardo|||0000-0002-1604-6244 Muñoz Guijosa, Juan Manuel|||0000-0003-3738-7643 |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Aeroderivative Gas Turbines Deep Neural Networks Fourier Transfor Fourier transform Rub Detection |
| topic |
Aeroderivative Gas Turbines Deep Neural Networks Fourier Transfor Fourier transform Rub Detection |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024 2024-10-01 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://oa.upm.es/84433/ |
| url |
https://oa.upm.es/84433/ |
| dc.language.none.fl_str_mv |
Inglés en |
| language_invalid_str_mv |
Inglés en |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.source.none.fl_str_mv |
reponame:Archivo Digital UPM instname:Universidad Politécnica de Madrid |
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Universidad Politécnica de Madrid |
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Archivo Digital UPM |
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Archivo Digital UPM |
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1869415427315073024 |
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15,812429 |