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dc.contributor.author | Zamudio-Ramirez, Israel | es_ES |
dc.contributor.author | Osornio-Rios, Roque A. | es_ES |
dc.contributor.author | Antonino-Daviu, Jose A. | es_ES |
dc.contributor.author | Cureño-Osornio, Jonathan | es_ES |
dc.contributor.author | Saucedo-Dorantes, Juan-Jose | es_ES |
dc.date.accessioned | 2022-10-05T18:03:11Z | |
dc.date.available | 2022-10-05T18:03:11Z | |
dc.date.issued | 2021-06 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/187087 | |
dc.description.abstract | [EN] Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz¿s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies. | es_ES |
dc.description.sponsorship | This work was supported by the Spanish Ministerio de Ciencia Innovación y Universidades and FEDER program in the framework of the `Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento¿ (ref: PGC2018-095747-B-I00), and Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Electronics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Bearing fault | es_ES |
dc.subject | Induction motor | es_ES |
dc.subject | Katz's fractal dimension | es_ES |
dc.subject | Linear discriminant analysis | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Stray flux | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/electronics10121486 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CONACYT//652815/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PGC2018-095747-B-I00-AR//TECNOLOGIAS AVANZADAS BASADAS EN EL ANALISIS DEL FLUJO DE DISPERSION EN REGIMEN TRANSITORIO PARA EL DIAGNOSTICO PRECOZ DE ANOMALIAS ELECTROMECANICAS EN MOTORES ELECTRICOS./ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica | es_ES |
dc.description.bibliographicCitation | Zamudio-Ramirez, I.; Osornio-Rios, RA.; Antonino-Daviu, JA.; Cureño-Osornio, J.; Saucedo-Dorantes, J. (2021). Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals. Electronics. 10(12):1-22. https://doi.org/10.3390/electronics10121486 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/electronics10121486 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 22 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 10 | es_ES |
dc.description.issue | 12 | es_ES |
dc.identifier.eissn | 2079-9292 | es_ES |
dc.relation.pasarela | S\439737 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Consejo Nacional de Ciencia y Tecnología, México | es_ES |