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dc.contributor.author | Antonino Daviu, José Alfonso | es_ES |
dc.date.accessioned | 2021-05-12T03:32:10Z | |
dc.date.available | 2021-05-12T03:32:10Z | |
dc.date.issued | 2020-09 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/166213 | |
dc.description.abstract | [EN] Electric motors condition monitoring is a field of paramount importance for industry. In recent decades, there has been a continuous effort to investigate on new techniques and methods that are able to determine the health of these machines with high accuracy and reliability. Classical methods based on the analysis of diverse machine quantities under stationary condition are being replaced by modern methodologies that are adapted to any operation regime of the machine (including transients). These new methods (especially those based on motor startup signal monitoring), that imply the use of advanced signal processing tools, have shown a great potential and have provided spectacular advantages versus conventional approaches enabling, among other facts, a much more reliable determination of the machine health. This paper reviews the background of this recent condition monitoring trend and shows the advantages of this new approach, with regards to its application to the analysis of electrical quantities. Examples referred to its application to real motors operating in industry are included, proving the huge potential of the transient-based approach and its benefits versus conventional methods. | es_ES |
dc.description.sponsorship | This research was funded by by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I + D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I + D + i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Applied Sciences | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Induction motor | es_ES |
dc.subject | Fault diagnosis | es_ES |
dc.subject | Electrical monitoring | es_ES |
dc.subject | Transient analysis | es_ES |
dc.subject | Rotor | es_ES |
dc.subject | Reliability | es_ES |
dc.subject | Predictive maintenance | es_ES |
dc.subject | Wavelet transforms | es_ES |
dc.subject | Current | es_ES |
dc.subject | Stray flux | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Electrical monitoring under transient conditions: a new paradigm in electric motors predictive maintenance | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/app10176137 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095747-B-I00/ES/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 | Antonino Daviu, JA. (2020). Electrical monitoring under transient conditions: a new paradigm in electric motors predictive maintenance. Applied Sciences. 10(17):1-16. https://doi.org/10.3390/app10176137 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app10176137 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 10 | es_ES |
dc.description.issue | 17 | es_ES |
dc.identifier.eissn | 2076-3417 | es_ES |
dc.relation.pasarela | S\417285 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
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