• español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Navegar

    Todo o repositórioComunidadesPor data do documentoAutoresAssuntosTítulos

    Minha conta

    Entrar

    Estatística

    Ver as estatísticas de uso

    Compartir

    Ver item 
    •   Página inicial
    • PRODUÇÃO CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería Eléctrica
    • DEP45 - Artículos de revista
    • Ver item
    •   Página inicial
    • PRODUÇÃO CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería Eléctrica
    • DEP45 - Artículos de revista
    • Ver item
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis

    Citas

    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/55762

    Título
    Condition monitoring of bearing faults using the stator current and shrinkage methods
    Autor
    Duque Pérez, ÓscarAutoridad UVA Orcid
    Pozo Gallego, Carlos del
    Moríñigo Sotelo, DanielAutoridad UVA Orcid
    Fontes Godoy, Wagner
    Año del Documento
    2019
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Energies, 2019, vol. 12, n. 17, 3392
    Resumo
    Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.
    Palabras Clave
    Condition monitoring
    Monitoreo de condición
    Machine learning
    Aprendizaje automático
    ISSN
    1996-1073
    Revisión por pares
    SI
    DOI
    10.3390/en12173392
    Patrocinador
    CAPES (process BEX552269/2011-5)
    Version del Editor
    https://www.mdpi.com/1996-1073/12/17/3392
    Propietario de los Derechos
    © 2019 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/55762
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP45 - Artículos de revista [47]
    Mostrar registro completo
    Arquivos deste item
    Nombre:
    Condition-monitoring-bearing-faults.pdf
    Tamaño:
    714.7Kb
    Formato:
    Adobe PDF
    Thumbnail
    Visualizar/Abrir
    Atribución 4.0 InternacionalExceto quando indicado o contrário, a licença deste item é descrito como Atribución 4.0 Internacional

    Universidad de Valladolid

    Powered by MIT's. DSpace software, Version 5.10