Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis

Chang Woo Hong; Kwangsuk Lee; Min Seung Ko; Jae Kyeong Kim; Kyungwon Oh; Kyeon Hur

doi:10.1109/BigComp48618.2020.00-98

Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis

Chang Woo Hong, Kwangsuk Lee, Min Seung Ko, Jae Kyeong Kim, Kyungwon Oh, Kyeon Hur

Department of Electrical and Electronic Engineering

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

9 Citations (Scopus)

Abstract

This paper proposes a deep model structure to improve the prediction accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation analysis that reduces the model complexity. The proposed model maximizes the performance by appropriately stacking one dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithm. It also includes residual network and dropout technique to improve the learning ability of the proposed model. The result of RUL forecasting using the proposed model is compared with that of various conventional methods, which demonstrates better efficiency yet high performance thanks to excluding the low correlated data.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
Editors	Wookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	63-70
Number of pages	8
ISBN (Electronic)	9781728160344
DOIs	https://doi.org/10.1109/BigComp48618.2020.00-98
Publication status	Published - 2020 Feb
Event	2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of Duration: 2020 Feb 19 → 2020 Feb 22

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

Conference

Conference	2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
Country/Territory	Korea, Republic of
City	Busan
Period	20/2/19 → 20/2/22

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number.2018R1D1A1A09083054).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems and Management
Control and Optimization

Access to Document

10.1109/BigComp48618.2020.00-98

Cite this

Hong, C. W., Lee, K., Ko, M. S., Kim, J. K., Oh, K., & Hur, K. (2020). Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. In W. Lee, L. Chen, Y-S. Moon, J. Bourgeois, M. Bennis, Y-F. Li, Y-G. Ha, H-Y. Kwon, & A. Cuzzocrea (Eds.), Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 (pp. 63-70). [9070439] (Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp48618.2020.00-98

Hong, Chang Woo ; Lee, Kwangsuk ; Ko, Min Seung et al. / Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020. editor / Wookey Lee ; Luonan Chen ; Yang-Sae Moon ; Julien Bourgeois ; Mehdi Bennis ; Yu-Feng Li ; Young-Guk Ha ; Hyuk-Yoon Kwon ; Alfredo Cuzzocrea. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 63-70 (Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020).

@inproceedings{b312af5fe69f4d209279f1f436856494,

title = "Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis",

abstract = "This paper proposes a deep model structure to improve the prediction accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation analysis that reduces the model complexity. The proposed model maximizes the performance by appropriately stacking one dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithm. It also includes residual network and dropout technique to improve the learning ability of the proposed model. The result of RUL forecasting using the proposed model is compared with that of various conventional methods, which demonstrates better efficiency yet high performance thanks to excluding the low correlated data.",

author = "Hong, {Chang Woo} and Kwangsuk Lee and Ko, {Min Seung} and Kim, {Jae Kyeong} and Kyungwon Oh and Kyeon Hur",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number.2018R1D1A1A09083054). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 ; Conference date: 19-02-2020 Through 22-02-2020",

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doi = "10.1109/BigComp48618.2020.00-98",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "63--70",

editor = "Wookey Lee and Luonan Chen and Yang-Sae Moon and Julien Bourgeois and Mehdi Bennis and Yu-Feng Li and Young-Guk Ha and Hyuk-Yoon Kwon and Alfredo Cuzzocrea",

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Hong, CW, Lee, K, Ko, MS, Kim, JK, Oh, K & Hur, K 2020, Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. in W Lee, L Chen, Y-S Moon, J Bourgeois, M Bennis, Y-F Li, Y-G Ha, H-Y Kwon & A Cuzzocrea (eds), Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020., 9070439, Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, Institute of Electrical and Electronics Engineers Inc., pp. 63-70, 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, Busan, Korea, Republic of, 20/2/19. https://doi.org/10.1109/BigComp48618.2020.00-98

Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. / Hong, Chang Woo; Lee, Kwangsuk; Ko, Min Seung et al.

Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020. ed. / Wookey Lee; Luonan Chen; Yang-Sae Moon; Julien Bourgeois; Mehdi Bennis; Yu-Feng Li; Young-Guk Ha; Hyuk-Yoon Kwon; Alfredo Cuzzocrea. Institute of Electrical and Electronics Engineers Inc., 2020. p. 63-70 9070439 (Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020).

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

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T1 - Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis

AU - Hong, Chang Woo

AU - Lee, Kwangsuk

AU - Ko, Min Seung

AU - Kim, Jae Kyeong

AU - Oh, Kyungwon

AU - Hur, Kyeon

N1 - Funding Information: ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number.2018R1D1A1A09083054). Publisher Copyright: © 2020 IEEE.

PY - 2020/2

Y1 - 2020/2

N2 - This paper proposes a deep model structure to improve the prediction accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation analysis that reduces the model complexity. The proposed model maximizes the performance by appropriately stacking one dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithm. It also includes residual network and dropout technique to improve the learning ability of the proposed model. The result of RUL forecasting using the proposed model is compared with that of various conventional methods, which demonstrates better efficiency yet high performance thanks to excluding the low correlated data.

AB - This paper proposes a deep model structure to improve the prediction accuracy of remaining useful life (RUL) of the turbofan engine by using the correlation analysis that reduces the model complexity. The proposed model maximizes the performance by appropriately stacking one dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM algorithm. It also includes residual network and dropout technique to improve the learning ability of the proposed model. The result of RUL forecasting using the proposed model is compared with that of various conventional methods, which demonstrates better efficiency yet high performance thanks to excluding the low correlated data.

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M3 - Conference contribution

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BT - Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

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A2 - Chen, Luonan

A2 - Moon, Yang-Sae

A2 - Bourgeois, Julien

A2 - Bennis, Mehdi

A2 - Li, Yu-Feng

A2 - Ha, Young-Guk

A2 - Kwon, Hyuk-Yoon

A2 - Cuzzocrea, Alfredo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

Y2 - 19 February 2020 through 22 February 2020

ER -

Hong CW, Lee K, Ko MS, Kim JK, Oh K, Hur K. Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. In Lee W, Chen L, Moon Y-S, Bourgeois J, Bennis M, Li Y-F, Ha Y-G, Kwon H-Y, Cuzzocrea A, editors, Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 63-70. 9070439. (Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020). doi: 10.1109/BigComp48618.2020.00-98

Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis

Abstract

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