Remaining useful life estimation using long short-term memory deep learning

Che Sheng Hsu, Jehn Ruey Jiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

42 引文 斯高帕斯(Scopus)

摘要

This paper proposes a deep learning method to estimate the remaining useful life (RUL) of aero-propulsion engines. The proposed method is based on the long short-term memory (LSTM) structure of the recurrent neural network (RNN). LSTM can effectively extract the relationship between data items that are far separated in the time series. The proposed method is applied to the NASA C-MAPSS data set for RUL estimation accuracy evaluation and is compared with the methods using the multi-layer perceptron (MLP), support vector regression (SVR), relevance vector regression (RVR) and convolutional neural network (CNN). Comparisons show that the proposed method is better than others in terms of the root mean squared error (RMSE) and the value of a scoring function.

原文???core.languages.en_GB???
主出版物標題Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
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58-61
頁數4
ISBN(電子)9781538643426
DOIs
出版狀態已出版 - 22 6月 2018
事件4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan
持續時間: 13 4月 201817 4月 2018

出版系列

名字Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

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???event.eventtypes.event.conference???4th IEEE International Conference on Applied System Innovation, ICASI 2018
國家/地區Japan
城市Chiba
期間13/04/1817/04/18

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