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

Juei En Lee; Jehn Ruey Jiang

doi:10.1109/ECICE47484.2019.8942782

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

Juei En Lee, Jehn Ruey Jiang

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

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).

Original language	English
Title of host publication	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Editors	Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	408-410
Number of pages	3
ISBN (Electronic)	9781728125015
DOIs	https://doi.org/10.1109/ECICE47484.2019.8942782
State	Published - Oct 2019
Event	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan Duration: 3 Oct 2019 → 6 Oct 2019

Publication series

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

Conference

Conference	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Country/Territory	Taiwan
City	Yunlin
Period	3/10/19 → 6/10/19

Keywords

bearing
convolutional neural network
deep learning
remaining useful life

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ECICE47484.2019.8942782

Cite this

Lee, J. E., & Jiang, J. R. (2019). Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation. In T.-H. Meen (Ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 (pp. 408-410). Article 8942782 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECICE47484.2019.8942782

Lee, Juei En ; Jiang, Jehn Ruey. / Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. editor / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 408-410 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

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title = "Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation",

abstract = "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).",

keywords = "bearing, convolutional neural network, deep learning, remaining useful life",

author = "Lee, {Juei En} and Jiang, {Jehn Ruey}",

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Lee, JE & Jiang, JR 2019, Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation. in T-H Meen (ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019., 8942782, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 408-410, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Yunlin, Taiwan, 3/10/19. https://doi.org/10.1109/ECICE47484.2019.8942782

Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation. / Lee, Juei En; Jiang, Jehn Ruey.
2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. p. 408-410 8942782 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jiang, Jehn Ruey

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N2 - 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).

AB - 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).

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KW - convolutional neural network

KW - deep learning

KW - remaining useful life

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M3 - 會議論文篇章

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Lee JE, Jiang JR. Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation. In Meen TH, editor, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 408-410. 8942782. (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). doi: 10.1109/ECICE47484.2019.8942782

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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