Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN

René Vinicio Sánchez, Pablo Lucero, Rafael E. Vásquez, Mariela Cerrada, Jean Carlo Macancela, Diego Cabrera

    Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

    40 Citas (Scopus)

    Resumen

    Gearboxes and bearings play an important role in industries for motion and torque transmission machines. Therefore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage to the machine or human losses; additionally, an appropriate diagnosis contributes to increase productivity and reduce maintenance costs. This paper addresses a methodological framework for the diagnosis of multi-faults in rotating machinery through the use of features rankings. The classification uses K nearest neighbors and random forest, based on the information that comes from the measured vibration signal. Thirty features in time domain are calculated from the vibration signal, twenty-four features commonly used in fault diagnosis in rotating machinery, and six features are used from the field of electromyography. Feature ranking methods such as ReliefF algorithm, Chi-Square, and Information Gain are used to select the ten most relevant features, the same ones that enter the classifiers. Five databases were used to validate the proposed methodological framework. The results show good accuracy in classification for the five databases; furthermore, in all the databases in the first ten features ranked by the three rankings methods are present at least two nonconventional features.

    Idioma originalInglés
    Páginas (desde-hasta)3463-3473
    Número de páginas11
    PublicaciónJournal of Intelligent and Fuzzy Systems
    Volumen34
    N.º6
    DOI
    EstadoPublicada - 2018

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