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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
EditorsChuan Li, Shaohui Zhang, Jianyu Long, Diego Cabrera, Ping Ding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages208-214
Number of pages7
ISBN (Electronic)9781728101996
DOIs
StatePublished - Aug 2019
Event2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 - Beijing, China
Duration: 15 Aug 201917 Aug 2019

Publication series

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

Conference

Conference2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
Country/TerritoryChina
CityBeijing
Period15/08/1917/08/19

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by the MOST Science and Technology Partnership Program (KY201802006), and Universidad Politécnica Salesiana through the research group GIDTEC.

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Deep learning
  • Faults classification
  • Gearboxes
  • Long short term memory networks
  • Vibration signals

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