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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/64937

    Título
    Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis
    Autor
    Martín Diaz, IgnacioAutoridad UVA
    Moríñigo Sotelo, DanielAutoridad UVA Orcid
    Duque Pérez, ÓscarAutoridad UVA Orcid
    Arredondo Delgado, P. A.
    Camarena Martínez, D.
    Romero Troncoso, René de Jesús
    Año del Documento
    2017-11
    Editorial
    Elsevier
    Documento Fuente
    Electric Power Systems Research, November 2017, 152, 18-26,
    Zusammenfassung
    Recently, there has been an increased interest in fault detection on electrical machines in steady-state regimes. Several frequency estimation techniques have been developed to assist the early detection of faults in induction motors, especially in line-fed motors. However, in modern industry, the use of inverters is increasingly present. This paper presents an analysis for comprehending the challenge in detecting incipient rotor faults using the stator current signal under different inverter supplies. The approach is based on the high-resolution technique known as multiple signal classification (MUSIC). In this study, incipient rotor faults in a squirrel-cage rotor, prior to the complete breaking of a rotor bar, are better identified in some inverters than others. The proposed approach finds the adequate MUSIC order that facilitates identification of bar breakage frequencies for early fault detection in each case studied from a wide set of trials. The study has been developed to detect incipient rotor bar breakages in an inverter-fed three-phase induction motor under varying load situations.
    ISSN
    0378-7796
    Revisión por pares
    SI
    DOI
    10.1016/j.epsr.2017.06.021
    Idioma
    spa
    URI
    https://uvadoc.uva.es/handle/10324/64937
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP45 - Artículos de revista [47]
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    Nombre:
    EPSR_2017.pdf
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    3.228Mb
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