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    • DEP45 - Artículos de revista
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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/61647

    Título
    Early detection of faults in induction motors—A review
    Autor
    García Calva, Tomás AlbertoAutoridad UVA
    Moríñigo Sotelo, DanielAutoridad UVA Orcid
    Fernández Cavero, Vanessa
    Romero Troncoso, René de Jesús
    Año del Documento
    2022
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Energies, 2022, Vol. 15, Nº. 21, 7855
    Abstract
    There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.
    Materias (normalizadas)
    Artificial intelligence
    Electric motors, Induction
    Motores de inducción
    Fault diagnosis
    Fault location (Engineering)
    Machine learning
    Aprendizaje automático
    Quality control
    Control de calidad
    Signal processing
    Procesamiento de señales
    Materias Unesco
    1203.04 Inteligencia Artificial
    3306.03 Motores Eléctricos
    3306 Ingeniería y Tecnología Eléctricas
    ISSN
    1996-1073
    Revisión por pares
    SI
    DOI
    10.3390/en15217855
    Version del Editor
    https://www.mdpi.com/1996-1073/15/21/7855
    Propietario de los Derechos
    © 2022 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/61647
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Collections
    • DEP45 - Artículos de revista [47]
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