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

Jehn Ruey Jiang, Chang Kuei Kuo

研究成果: 書貢獻/報告類型會議論文篇章同行評審

10 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
主出版物子標題Information and Innovation for Modern Technology, ICICE 2017
編輯Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面120-123
頁數4
ISBN(電子)9781538632024
DOIs
出版狀態已出版 - 1 10月 2018
事件2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China
持續時間: 17 11月 201720 11月 2017

出版系列

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

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???event.eventtypes.event.conference???2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
國家/地區China
城市Xiamen, Fujian
期間17/11/1720/11/17

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