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dc.contributor.author | Fontes Godoy, Wagner | |
dc.contributor.author | Moríñigo Sotelo, Daniel | |
dc.contributor.author | Duque Pérez, Óscar | |
dc.contributor.author | Nunes da Silva, Ivan | |
dc.contributor.author | Goedtel, Alessandro | |
dc.contributor.author | Palácios, Rodrigo Henrique Cunha | |
dc.date.accessioned | 2023-03-29T08:38:59Z | |
dc.date.available | 2023-03-29T08:38:59Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Energies 2020, vol. 13, n. 13, 3481 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/59046 | |
dc.description | Producción Científica | es |
dc.description.abstract | This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Ingeniería eléctrica | es |
dc.subject.classification | Diagnosis | es |
dc.subject.classification | Bearing faults | es |
dc.subject.classification | Intelligent estimation | es |
dc.subject.classification | Three-phase induction motor | es |
dc.subject.classification | Diagnóstico | es |
dc.subject.classification | Fallas en rodamientos | es |
dc.subject.classification | Estimación inteligente | es |
dc.title | Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2020 The Authors | es |
dc.identifier.doi | 10.3390/en13133481 | es |
dc.relation.publisherversion | https://www.mdpi.com/1996-1073/13/13/3481 | es |
dc.identifier.publicationfirstpage | 3481 | es |
dc.identifier.publicationissue | 13 | es |
dc.identifier.publicationtitle | Energies | es |
dc.identifier.publicationvolume | 13 | es |
dc.peerreviewed | SI | es |
dc.description.project | CAPES (process BEX552269/2011-5) | es |
dc.description.project | National Council for Scientific and Technological Development (grant #474290/2008-3, #473576/2011-2, #552269/2011-5, #307220/2016-8) | es |
dc.identifier.essn | 1996-1073 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 3313 Tecnología E Ingeniería Mecánicas | es |
dc.subject.unesco | 3306 Ingeniería y Tecnología Eléctricas | es |
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