Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation

Juei En Lee, Jehn Ruey Jiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

4 引文 斯高帕斯(Scopus)

摘要

This paper proposes a deep learning method, called time series multi-channel convolutional neural network (TSMC-CNN), for remaining useful life (RUL) estimation of bearings. The time series data of bearing operation are divided into multiple channels to be fed into the convolutional neural network (CNN) to extract relationship between far apart data points. The PRONOSTIA bearing operation datasets are used to evaluate the proposed method performance. The evaluation results are compared with those of related methods to show the superiority of the proposed method in terms of the root mean squared error (RMSE) and the mean absolute error (MAE).

原文???core.languages.en_GB???
主出版物標題2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面408-410
頁數3
ISBN(電子)9781728125015
DOIs
出版狀態已出版 - 10月 2019
事件2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan
持續時間: 3 10月 20196 10月 2019

出版系列

名字2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

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???event.eventtypes.event.conference???2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
國家/地區Taiwan
城市Yunlin
期間3/10/196/10/19

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