Multi-fault diagnosis of rotating machinery by using feature ranking methods and SVM-based classifiers

Rene Vinicio Sanchez, Pablo Lucero, Jean Carlo Macancela, Mariela Cerrada, Rafael E. Vasquez, Fannia Pacheco

    Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

    13 Citas (Scopus)

    Resumen

    Rotating machinery plays an important role in industries for motion transmission in machines; the breakdowns of gearboxes are mostly produced by gear and bearings failures. Thus, some strategies are sought to avoid unscheduled stops, or catastrophic damages, in order to reduce maintenance costs and increase reliability. This paper describes a methodological framework to detect eleven rotating machinery faults by using feature ranking methods and support vector machine, based on information that comes from the measured vibration signal. Thirty features are calculated from the vibration signal in time domain, for each faulty condition. Feature ranking methods such as ReliefF, Chi square, and Information Gain are used to select the most informative features, and subsequently to reduce the size of the feature vector. The feature ranking methods are compared in order to obtain improved diagnosis results with a reduced feature set. Results show good fault identification accuracy with the first four features of ReliefF ranking method as input to support vector machine classifier.

    Idioma originalInglés
    Título de la publicación alojadaProceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
    EditoresWei Guo, Jose Valente de Oliveira, Chuan Li, Yun Bai, Ping Ding, Juanjuan Shi
    EditorialInstitute of Electrical and Electronics Engineers Inc.
    Páginas105-110
    Número de páginas6
    ISBN (versión digital)9781509040209
    DOI
    EstadoPublicada - 9 dic. 2017
    Evento2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 - Shanghai, China
    Duración: 16 ago. 201718 ago. 2017

    Serie de la publicación

    NombreProceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
    Volumen2017-December

    Conferencia

    Conferencia2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
    País/TerritorioChina
    CiudadShanghai
    Período16/08/1718/08/17

    Nota bibliográfica

    Publisher Copyright:
    © 2017 IEEE.

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