A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

LI Juan; DAI Hongde; JING Bo; JIAO Xiaoxuan

doi:10.1049/cje.2020.11.004

Volume 30 Issue 1

Jan. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(1): 36-44

LI Juan, DAI Hongde, JING Bo, et al., “A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 36-44, 2021, doi: 10.1049/cje.2020.11.004

Citation:

LI Juan, DAI Hongde, JING Bo, et al., “A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 36-44, 2021, doi: 10.1049/cje.2020.11.004

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A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

doi: 10.1049/cje.2020.11.004

LI Juan^{1
,
,},
DAI Hongde^2
,,
JING Bo^3
,,
JIAO Xiaoxuan^3
,

1.
College of Mathematics and Statistics, Ludong University, Yantai 264025, China
2.
School of Basic Sciences for Aviation, Naval Aviation University, Yantai 264001, China
3.
College of Aeronautics Engineering, Air Force Engineering University, Xi'an 710038, China

Funds:

the Shandong Natural Science Foundation of China ZR2017MF036

Defense Science and Technology Project Foundation of China 2019-JCJQ-JJ-059

More Information

Author Bio:
DAI Hongde   was born in 1981. He received the Ph.D. degree in 2008 from Northwestern Polytechnical University, now he is an Associate Professor with School of Basic Sciences for Aviation, Naval Aviation University. His main research interests include inertial technology and integrated navigation, filtering estimation theory, and reliability theory. (Email: dihod@126.com)

JING Bo   was born in 1965. She is a Professor, PHD supervisor in Air Force Engineering University. Her interests include Prognostic and health management, Sensor Network and reliability theory. (Email: jingbo_sensor@163.com)

JIAO Xiaoxuan   received the B.S., M.S., and Ph.D. degrees from Air Force Engineering University in 2012, 2014 and 2019, respectively. He is now a lecturer at Air Force Engineering University. His main research interests include information fusion, fault diagnosis and prognostics. (Email: 564325155@qq.com)
Corresponding author: LI Juan (corresponding author) was born in 1981, she received the Ph.D. degree in Air Force Engineering University, China. Now she is an Associate Professor in Ludong University. Her main research interests include prognostic and health management, statistic analysis and reliability theory. (Email: daidaiquanquan123@126.com)
Received Date: 2019-12-02
Accepted Date: 2020-04-26
Publish Date: 2021-01-01

Abstract

Abstract

Feature extraction plays an important role in Remaining useful life (RUL) prediction. Feature extraction mainly depends on the performance degradation signal in the previous study, in which the dynamic correlations among different signals are ignored, and the RUL accuracy is affected. A new dynamic feature based on the correlations of the performance degradation signal is proposed. First, dynamic correlation coefficients are calculated by copula function as the multivariate correlation performance degradation features. Second, the random effect Wiener process is used for RUL prediction based on the new features, and the maximum likelihood estimation is adopted to calculate the unknown parameters of the Wiener process. Finally, the RUL estimation for solder joints under vibration load is carried out compared with the quantile and quantile-Principal component analysis (PCA) mixed feature extraction method. The research results show that the proposed method improved the prediction accuracy of RUL.
- Remaining useful life,
- Dynamic copula,
- Random effect Wiener-process,
- Solder joints

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References(36)

References

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