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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/5575

    Título
    Early and extremely early multi-label fault diagnosis in induction motors
    Autor
    Juez Gil, Mario
    Saucedo Dorantes, Juan José
    Arnaiz González, Álvaruntranslated Orcid
    López Nozal, Carlosuntranslated Orcid
    García Osorio, Césaruntranslated Orcid
    Lowe, David
    Publicado en
    ISA Transactions. 2020, V. 106, p.367-381
    Editorial
    Elsevier
    Fecha de publicación
    2020-11
    ISSN
    0019-0578
    DOI
    10.1016/j.isatra.2020.07.002
    Abstract
    The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification.
    Palabras clave
    Multi-fault detection
    Early detection
    Multi-label classification
    Principal component analysis
    Load insensitive model
    Prediction at low operating frequencies
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/5575
    Versión del editor
    https://doi.org/10.1016/j.isatra.2020.07.002
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