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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
EditorsWookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-70
Number of pages8
ISBN (Electronic)9781728160344
DOIs
Publication statusPublished - 2020 Feb
Event2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of
Duration: 2020 Feb 192020 Feb 22

Publication series

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

Conference

Conference2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
CountryKorea, Republic of
CityBusan
Period20/2/1920/2/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems and Management
  • Control and Optimization

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  • 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