Enhancing convolutional neural network deep learning for remaining useful life estimation in smart factory applications

Jehn Ruey Jiang, Chang Kuei Kuo

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

3 Scopus citations

Abstract

Estimating the remaining useful life (RUL) of machines or components is essential for prognostics and health management (PHM) in smart factories. This paper enhances the convolutional neural network (CNN) deep learning for RUL estimation in smart factory applications. The enhanced CNN deep learning is applied to NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set to estimate the RUL of aero-propulsion engines. It is shown to have better performance than other related methods.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
Subtitle of host publicationInformation and Innovation for Modern Technology, ICICE 2017
EditorsArtde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages120-123
Number of pages4
ISBN (Electronic)9781538632024
DOIs
StatePublished - 1 Oct 2018
Event2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China
Duration: 17 Nov 201720 Nov 2017

Publication series

NameProceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017

Conference

Conference2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
Country/TerritoryChina
CityXiamen, Fujian
Period17/11/1720/11/17

Keywords

  • Convolutional neural network
  • Deep learning
  • Remaining useful life
  • Smart factory

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