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Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals

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Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals

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dc.contributor.author Iglesias-Martínez, Miguel E. es_ES
dc.contributor.author Guerra Carmenate, Jose es_ES
dc.contributor.author Antonino-Daviu, J. es_ES
dc.contributor.author Dunai, Larisa es_ES
dc.contributor.author Platero, Carlos A. es_ES
dc.contributor.author Conejero, J. Alberto es_ES
dc.contributor.author Fernández de Córdoba, Pedro es_ES
dc.date.accessioned 2024-04-19T18:03:05Z
dc.date.available 2024-04-19T18:03:05Z
dc.date.issued 2023-08 es_ES
dc.identifier.issn 0093-9994 es_ES
dc.identifier.uri http://hdl.handle.net/10251/203634
dc.description.abstract [EN] In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications. es_ES
dc.description.sponsorship Miguel E. Iglesias Martínez s work was supported by the postdoctoral research scholarship "Ayudas para la recualificación del sistema universitario español 2021-2023. Modalidad: Margarita Salas", UPV, Ministerio de Universidades, Plan de Recuperación, Transformación y Resiliencia, Spain. Funded by the European Union-Next Generation EU. This work is also supported by Generalitat Valenciana (reference CIAICO/2021/020) es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Transactions on Industry Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bicoherence es_ES
dc.subject Motors es_ES
dc.subject Skewness-Kurtosis es_ES
dc.subject Flux es_ES
dc.subject Winding Faults es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/TIA.2023.3262220 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//CIAICO%2F2021%2F020//DESARROLLO DE TÉCNICAS INTELIGENTES BASADAS EN ANÁLISIS COMBINADO DE CORRIENTES Y FLUJOS PARA EL DIAGNÓSTICO DE NUEVAS TIPOLOGÍAS DE FALLO Y CONDICIONES DE OPERACIÓN EN MOTORES DE INDUCCIÓN/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Pura y Aplicada - Institut Universitari de Matemàtica Pura i Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Iglesias-Martínez, ME.; Guerra Carmenate, J.; Antonino-Daviu, J.; Dunai, L.; Platero, CA.; Conejero, JA.; Fernández De Córdoba, P. (2023). Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals. IEEE Transactions on Industry Applications. 59(4):3945-3954. https://doi.org/10.1109/TIA.2023.3262220 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/TIA.2023.3262220 es_ES
dc.description.upvformatpinicio 3945 es_ES
dc.description.upvformatpfin 3954 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 59 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\486083 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES


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