Volume 30 Issue 1
Jan.  2021
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Article Contents
LI Juan, DAI Hongde, JING Bo, JIAO Xiaoxuan. A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction[J]. Chinese Journal of Electronics, 2021, 30(1): 36-44. doi: 10.1049/cje.2020.11.004
Citation: LI Juan, DAI Hongde, JING Bo, JIAO Xiaoxuan. A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction[J]. Chinese Journal of Electronics, 2021, 30(1): 36-44. doi: 10.1049/cje.2020.11.004

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

doi: 10.1049/cje.2020.11.004

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
  • 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.
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  • [1]
    B. Zhang, L. Xu, Y. Chen, et al., "Remaining useful life based maintenance policy for deteriorating systems subject to continuous degradation and shock", Procedia IRP, Vol. 72, No. 1, pp. 1311–1315, 2018. http://www.sciencedirect.com/science/article/pii/S2212827118303664
    S. T. Tseng, M. Hamada and C. H. Chiao, "Using degradation data to improve fluorescent lamp reliability", Journal of Quality Technology, Vol. 27, No. 4, pp. 363–369, 1995. doi: 10.1080/00224065.1995.11979618
    Y. Peng and D. T. Liu, "Data-driven prognostics and health management: A review of recent advances", Chinese Journal of Scientific Instrument, Vol. 35, No. 3, pp. 481–495, 2014. (in Chinese) http://www.cqvip.com/main/zcps.aspx?c=1&id=48975645
    X. S. Si, W. Wang, C. H. Hu, et al., "Remaining useful life estimation–A review on the statistical data driven approaches", European Journal of Operational Research, Vol. 213, No. 1, pp. 1–14, 2011. doi: 10.1016/j.ejor.2010.11.018
    K. T. P. Nguyen, M. Fouladirad and A. Grall, "Model selection for degradation modeling and prognosis with health monitoring data", Reliability Engineering & System Safety, Vol. 169, No. 1, pp. 105–116, 2018. http://www.sciencedirect.com/science/article/pii/S0951832017302612
    D. Barraza-Barraza, V. G. Tercero-Gómez, M. G. Beruvides, et al., "An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth", Mechanical Systems and Signal Processing, Vol. 82, No. 1, pp. 519–536, 2017.
    X. Wang, S. Lin, S. Wang, et al., "Remaining useful life prediction based on the Wiener process for an aviation axial piston pump", Chinese Journal of Aeronautics, Vol. 29, No. 3, pp. 779–788, 2016. doi: 10.1016/j.cja.2015.12.020
    J. Li, B. Jing, H. D. Dai, et al., "Remaining useful life prediction based on variation coefficient consistency test of a Wiener process", Chinese Journal of Aeronautics, Vol. 31, No. 1, pp. 107–116, 2018. doi: 10.1016/j.cja.2017.11.001
    J. X. Zhang, C. H. Hu, Z. X. Zhou, et al., "Multiple degradation variables modeling for remaining useful life estimation of gyros based on copula function", Acta Aeronautica et Astronautica Sinica, Vol. 35, No. 4, pp. 1111–1121, 2014. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-HKXB201404023.htm
    S. N. Liu, N. Y. Lu, YH. Cheng, et al., "Remaining life time prediction for momentum wheel based on multiple degradation parameters", Journal of Nanjing University of Aeronautics & Astronautics, Vol. 47, No. 3, pp. 360–366, 2015. http://www.researchgate.net/publication/298656914_Remaining_lifetime_prediction_for_momentum_wheel_based_on_multiple_degradation_parameters
    Y. L. Tse, M. E. Cholette and P. W. Tse, "A multi-sensor approach to remaining useful life estimation for a slurry pump", Measurement, Vol. 139, No. 3, pp. 140–151, 2019. http://www.sciencedirect.com/science/article/pii/S0263224119301976
    J. Pan, X. Y. Wang, W. H. Chen, et al., "Statistical analysis on accelerated degradation test data based on multiple performance parameters", Advanced Materials Research, Vol. 430, No. 1, pp. 1417–1423, 2012. http://www.scientific.net/AMR.430-432.1417
    F. N. Zhou, J. H. Park, C. L. Wen, et al., "Average accumulative based time variant model for early diagnosis and prognosis of slowly varying faults", Sensors, Vol. 18, No. 6, pp. 1804–1811, 2018. doi: 10.3390/s18061804
    P. Kundu, A. K. Darpe and M. S. Kulkarni, "Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions", Mechanical Systems and Signal Processing, Vol. 134, https://doi.org/10.1016/j.ymssp.2019.106302, 2019.
    K. L. Son, M. Fouladirad, A. Barros, et al., "Remaining useful life estimation based on stochastic deterioration models: A comparative study", Reliability Engineering & System Safety, Vol. 112, No. 4, pp. 165–175, 2013. http://www.sciencedirect.com/science/article/pii/S0951832012002529
    Q. H. Zhong, Z. H. Zhang and S. J. Liang, "Reliability analysis approach based on multivariate degradation data", Systems Engineering-Theory & Practice, Vol. 31, No. 3, pp. 544–551, 2011. (in Chinese)
    C. Li and H. Hao, "A copula-based degradation modeling and reliability assessment", Engineering Letters, Vol. 24, No. 1, pp. 295–300, 2016. http://www.researchgate.net/publication/308052227_A_copula-based_degradation_modeling_and_reliability_assessment
    J. K. Sari, "Multivariate degradation modelling and its application to reliability testing", Master's Thesis, Parahyangan Catholic Universit, 2008.
    Z. Q. Pan, "Bivariate degradation reliability modeling and experimental design approaches under accelerating stress scenarios", Ph. D. Thesis, National University of Defense Technology, 2011. (in Chinese)
    X. Wang, B. Guo and Z. Cheng, "Residual life estimation based on bivariate Wiener degradation process with time-scale transformations", Journal of Statistical Computation & Simulation, Vol. 84, No. 3, pp. 545–563, 2014. doi: 10.1080/00949655.2012.719026
    Z. Xi, R. Jing, P. Wang, et al., "A copula-based sampling method for data-driven prognostics", Reliability Engineering & System Safety, Vol. 132, No. 11, pp. 72–82, 2014.
    Z. Xi and X. Zhao, "An enhanced copula-based method for data-driven prognostics considering insufficient training units", Reliability Engineering & System Safety, Vol. 188, No. 3, pp. 181–194, 2019. http://www.sciencedirect.com/science/article/pii/S0951832018310536
    Q. G. Hu and S. Zhou, "Reliability analysis of failure dynamic mechanical system usingvine copula model", Mechanical Science and Technology for Aerospace Engineering, Vol. 37, No. 8, pp. 1149–1155, 2018. (in Chinese) http://search.cnki.net/down/default.aspx?filename=JXKX201808002&dbcode=CJFD&year=2018&dflag=pdfdown
    C. Zhang, L. Pan, S. Wang, et al., "An accelerated life test model for solid lubricated bearings used in space based on time-varying dependence analysis of different failure models", Acta Astronautica, Vol. 152, No. 8, pp. 352–359, 2018. (in Chinese) http://www.sciencedirect.com/science/article/pii/S0094576518308476
    J. A. Patton, "Modelling asymmetric exchange rate dependence", International Economic Review, Vol. 47, No. 2, pp. 527–556, 2006. doi: 10.1111/j.1468-2354.2006.00387.x
    I. D. L. Salvatierra and A. J. Patton, "Dynamic copula models and high frequency data", Journal of Empirical Finance, Vol. 30, No. 1, pp. 120–135, 2015.
    J. X. Hu, B. Jing, Z. J. Sheng, et al., "Failure and failure characterization of QFP package interconnect structure under random vibration condition", Microelectronics Reliability, Vol. 91, No. 11, pp. 120–127, 2018. http://www.sciencedirect.com/science/article/pii/S0026271418302610
    F. X. Che and J. H. L. Pang, "Study on reliability of PQFP assembly with lead free solder joints under random vibration test", Microelectronics Reliability, Vol. 55, No. 12, pp. 2769–2776, 2015. doi: 10.1016/j.microrel.2015.09.010
    B. R. Nelsen, An Introduction to Copulas, New York: Springer, 2006.
    J. Li, B. Jing, Z. J. Sheng, et al., "GARCH based degradation modeling of solder joint under vibration loading", Prognostics and System Health Management Conference, IEEE, Harbin, China, pp. 1–5, 2017.
    Z. S. Ye, N. Chen and Y. Shen, "A new class of Wiener process models for degradation analysis", Reliability Engineering & System Safety, Vol. 139, No. 7, pp. 58–67, 2015. http://www.sciencedirect.com/science/article/pii/s0951832015000502
    C. Y. Pengand S. T. Tseng, "Mis-specification analysis of linear degradation models", IEEE Transactions on Reliability, Vol. 58, No. 3, pp. 444–455, 2009. doi: 10.1109/TR.2009.2026784
    W. Tang, B. Jing, Y. F. Huang, et al., "Multistate degradation model for prognostics of solder joints under vibration conditions", Chinese Journal of Electronics, Vol. 25, No. 4, pp. 779–785, 2016. doi: 10.1049/cje.2016.07.012
    W. Tang, B. Jing, Y. F. Huang, et al., "Feature extraction for latent fault detection and failure modes classification of board-level package under vibration loadings", Science China Technological Sciences, Vol. 58, No. 11, pp. 1905–1914, 2016.
    J. Li, B. Jing, X. Q. Qiang, et al., "Fault states feature extraction and experimental study for airborne fuel pumps based on sample quantile", Acta Aeronautica et Astronautica Sinica, Vol. 37, No. 9, pp. 2851–2863, 2016.
    C. Y. Peng and S. T. Tseng, "Mis-specification analysis of linear degradation models", IEEE Transactions on Reliability, Vol. 58, No. 3, pp. 444–455, 2009. doi: 10.1109/TR.2009.2026784
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