Standard
Remaining useful life prediction of rotating machinery using hierarchical deep neural network. /
Xia, Min; Li, Teng; Liu, Lizhi et al.
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2778-2783 (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Xia, M, Li, T, Liu, L, Xu, L, Gao, S & De Silva, CW 2017,
Remaining useful life prediction of rotating machinery using hierarchical deep neural network. in
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2778-2783, 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, Canada,
5/10/17.
https://doi.org/10.1109/SMC.2017.8123047
APA
Xia, M., Li, T., Liu, L., Xu, L., Gao, S., & De Silva, C. W. (2017).
Remaining useful life prediction of rotating machinery using hierarchical deep neural network. In
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (pp. 2778-2783). (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/SMC.2017.8123047
Vancouver
Xia M, Li T, Liu L, Xu L, Gao S, De Silva CW.
Remaining useful life prediction of rotating machinery using hierarchical deep neural network. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2778-2783. (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017). Epub 2017 Oct 8. doi: 10.1109/SMC.2017.8123047
Author
Xia, Min ; Li, Teng ; Liu, Lizhi et al. /
Remaining useful life prediction of rotating machinery using hierarchical deep neural network. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2778-2783 (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017).
Bibtex
@inproceedings{7dc51082552945b79668e413ab4e80d7,
title = "Remaining useful life prediction of rotating machinery using hierarchical deep neural network",
abstract = "This paper presents a novel approach for remaining useful life (RUL) prediction of rotating machinery using hierarchical deep neural networks (DNN). The different health stages are classified by a DNN-based health stage classifier trained by segmented degradation signal. This method builds several RUL predictors based on the health stages of the degradation process. Instead of modeling the entire degradation process (typically including various stages with dramatically different properties) with a single model, the proposed approach builds RUL model for each health stage where more accurate fitting can be obtained. A smoothing operator is applied to obtain the final RUL prediction. The experimental results show that the proposed method can achieve more accurate RUL prediction.",
author = "Min Xia and Teng Li and Lizhi Liu and Lin Xu and Shujun Gao and {De Silva}, {Clarence W.}",
year = "2017",
month = nov,
day = "27",
doi = "10.1109/SMC.2017.8123047",
language = "English",
series = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2778--2783",
booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
note = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 ; Conference date: 05-10-2017 Through 08-10-2017",
}
RIS
TY - GEN
T1 - Remaining useful life prediction of rotating machinery using hierarchical deep neural network
AU - Xia, Min
AU - Li, Teng
AU - Liu, Lizhi
AU - Xu, Lin
AU - Gao, Shujun
AU - De Silva, Clarence W.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - This paper presents a novel approach for remaining useful life (RUL) prediction of rotating machinery using hierarchical deep neural networks (DNN). The different health stages are classified by a DNN-based health stage classifier trained by segmented degradation signal. This method builds several RUL predictors based on the health stages of the degradation process. Instead of modeling the entire degradation process (typically including various stages with dramatically different properties) with a single model, the proposed approach builds RUL model for each health stage where more accurate fitting can be obtained. A smoothing operator is applied to obtain the final RUL prediction. The experimental results show that the proposed method can achieve more accurate RUL prediction.
AB - This paper presents a novel approach for remaining useful life (RUL) prediction of rotating machinery using hierarchical deep neural networks (DNN). The different health stages are classified by a DNN-based health stage classifier trained by segmented degradation signal. This method builds several RUL predictors based on the health stages of the degradation process. Instead of modeling the entire degradation process (typically including various stages with dramatically different properties) with a single model, the proposed approach builds RUL model for each health stage where more accurate fitting can be obtained. A smoothing operator is applied to obtain the final RUL prediction. The experimental results show that the proposed method can achieve more accurate RUL prediction.
U2 - 10.1109/SMC.2017.8123047
DO - 10.1109/SMC.2017.8123047
M3 - Conference contribution/Paper
AN - SCOPUS:85044239659
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 2778
EP - 2783
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -
