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dc.contributor.authorMoríñigo Sotelo, Daniel 
dc.contributor.authorRomero-Troncoso, René de Jesús
dc.contributor.authorPanagiotou, P. A.
dc.contributor.authorAntonino Daviu, Jose A.
dc.contributor.authorGyftakis, K. N.
dc.date.accessioned2024-01-20T16:44:36Z
dc.date.available2024-01-20T16:44:36Z
dc.date.issued2018
dc.identifier.citationIEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1224-1234, March-April 2018es
dc.identifier.issn1939-9367es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/64786
dc.description.abstractInduction motors are used in a variety of industrial applications where frequent startup cycles are required. In those cases, it is necessary to apply sophisticated signal processing analysis methods in order to reliably follow the time evolution of the fault-related harmonics in the signal.In this paper, the zero-sequence current (ZSC) is analysed using the high-resolution spectral method of multiple signal classification (MUSIC). The analysis of the ZSC signal has proved to have several advantages over the analysis of a single-phase current waveform. The method is validated through simulation and experimental results. The simulations are carried out for a 1.1 MW and a 4kW induction motors under finite element analysis (FEA). Experimentation is performed on a healthy motor, a motor with one broken rotor bar, and a motor with two broken rotor bars. The analysis results are satisfactory since the proposed methodology reliably detects the broken rotor bar fault and its severity, both during transient and steady state operation of the induction motor.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherThe IEEEes
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.subject.classificationBroken bares
dc.subject.classificationFault diagnosises
dc.subject.classificationInduction motores
dc.subject.classificationMUSICes
dc.subject.classificationZSCes
dc.titleReliable Detection of Rotor Bars Breakage in Induction Motors via MUSIC and ZSCes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderThe IEEEes
dc.identifier.doihttps://doi.org/10.1109/TIA.2017.2764846es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8074748es
dc.identifier.publicationfirstpage1124es
dc.identifier.publicationissue2es
dc.identifier.publicationlastpage1234es
dc.identifier.publicationvolume54es
dc.peerreviewedSIes
dc.description.projectThis work was supported in part by the Spanish ‘Ministerio de Economía y Competitividad’ (MINECO) and FEDER program in the framework of the ‘Proyectos I+D del Subprograma de Generación de Conocimiento, Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia’ (ref: DPI2014-52842-P)es
dc.type.hasVersioninfo:eu-repo/semantics/draftes


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