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

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
CountryChina
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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