A LSTM Neural Network Approach using Vibration Signals for Classifying Faults in a Gearbox

Ruben Medina, Jean Carlo Macancela, Pablo Lucero, Diego Cabrera, Chuan Li, Mariela Cerrada, Rene Vinicio Sanchez, Rafael E. Vasquez

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

    1 Cita (Scopus)

    Resumen

    A deep learning based method for classifying multi-class faults in a gearbox is presented. A set of 900 vibration signals representing the normal condition and nine faults comprises the dataset used in this research. The recorded vibration signals are pre-processed for extracting the first and second derivatives as well as the first five Intrinsic Mode Functions (IMFs) by applying the Empirical Mode Decomposition (EMD) method. A 2D representation of these signals is the feature space used for classifying ten conditions of a gearbox using a Long Short Term Memory (LSTM) neural network. The 2D feature space is subdivided along the temporal axis in segments of the same size as the LSTM network. These segments are classified and a voting systems is proposed for attaining the signal classification. A 10-fold cross-validation is used for evaluating the proposed deep learning model. An average accuracy up to 99.4 % for classifying the faults is attained during the cross-validation.

    Idioma originalInglés
    Título de la publicación alojadaProceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
    EditoresChuan Li, Shaohui Zhang, Jianyu Long, Diego Cabrera, Ping Ding
    EditorialInstitute of Electrical and Electronics Engineers Inc.
    Páginas208-214
    Número de páginas7
    ISBN (versión digital)9781728101996
    DOI
    EstadoPublicada - ago. 2019
    Evento2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 - Beijing, China
    Duración: 15 ago. 201917 ago. 2019

    Serie de la publicación

    NombreProceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

    Conferencia

    Conferencia2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
    País/TerritorioChina
    CiudadBeijing
    Período15/08/1917/08/19

    Nota bibliográfica

    Publisher Copyright:
    © 2019 IEEE.

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