Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/129246
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dc.contributor.authorMonzón-Verona, José Miguelen_US
dc.contributor.authorGonzález Domínguez, Pabloen_US
dc.contributor.authorGarcía-Alonso, Santiagoen_US
dc.date.accessioned2024-03-07T09:25:46Z-
dc.date.available2024-03-07T09:25:46Z-
dc.date.issued2024en_US
dc.identifier.issn1424-8220en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/129246-
dc.description.abstractIn this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.en_US
dc.languageengen_US
dc.relation.ispartofSensors (Switzerland)en_US
dc.sourceSensors[ISSN 1424-8220],v. 24 (4), (Febrero 2024)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherCmos Image Sensoren_US
dc.subject.otherConvolutional Neural Networken_US
dc.subject.otherDeep Learningen_US
dc.subject.otherMineral Oilsen_US
dc.subject.otherNon-Destructive Diagnosisen_US
dc.subject.otherPartial Dischargesen_US
dc.titleCharacterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networksen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s24041317en_US
dc.identifier.pmid38400475-
dc.identifier.scopus85185540865-
dc.identifier.isi001172194600001-
dc.contributor.orcidNO DATA-
dc.contributor.orcidNO DATA-
dc.contributor.orcidNO DATA-
dc.contributor.authorscopusid26531597500-
dc.contributor.authorscopusid57203973366-
dc.contributor.authorscopusid35106946100-
dc.identifier.eissn1424-8220-
dc.identifier.issue4-
dc.relation.volume24en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid26020084-
dc.contributor.daisngid44365002-
dc.contributor.daisngid55756570-
dc.description.numberofpages35en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Monzón-Verona, JM-
dc.contributor.wosstandardWOS:González-Domínguez, P-
dc.contributor.wosstandardWOS:García-Alonso, S-
dc.date.coverdateFebrero 2024en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,786-
dc.description.jcr3,4-
dc.description.sjrqQ1-
dc.description.jcrqQ2-
dc.description.scieSCIE-
dc.description.miaricds10,8-
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IUMA: Instrumentación avanzada-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Eléctrica-
crisitem.author.deptGIR IUMA: Instrumentación avanzada-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Eléctrica-
crisitem.author.deptGIR IUMA: Instrumentación avanzada-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.orcid0000-0001-9694-269X-
crisitem.author.orcid0000-0003-4389-0632-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.fullNameMonzón Verona, José Miguel-
crisitem.author.fullNameGonzález Domínguez, Pablo-
crisitem.author.fullNameGarcia-Alonso Montoya, Santiago-
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