A comparative feature analysis for gear pitting level classification by using acoustic emission, vibration and current signals

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

    Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

    8 Citas (Scopus)

    Resumen

    this paper addresses the comparison of features, extracted in the time domain, from vibration, acoustic emission, and current signals, for the identification of eight levels of severity of pitting in a gearbox. The vibration, acoustic emission, and current signals were first acquired using a gearbox lab experimental test bed. Then, twenty features were extracted in the time domain from each signal; these features are ranked by Chi squared and entered into the KNN classifier, which allows the evaluation of the classification accuracy for each acquired signal and performing an analysis of the features. The results indicate that the vibration and AE signals identified the pitting level better than the current signal.

    Idioma originalInglés
    Páginas (desde-hasta)346-352
    Número de páginas7
    Publicación10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2018: Warsaw, Poland, 29-31 August 2018
    Volumen51
    N.º24
    DOI
    EstadoPublicada - 2018

    Nota bibliográfica

    Publisher Copyright:
    © 2018

    Huella

    Profundice en los temas de investigación de 'A comparative feature analysis for gear pitting level classification by using acoustic emission, vibration and current signals'. En conjunto forman una huella única.

    Citar esto