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A copula-based sampling method for data-driven prognostics

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  • Xi, Zhimin
  • Jing, Rong
  • Wang, Pingfeng
  • Hu, Chao

Abstract

This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

Suggested Citation

  • Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
    • Handle: RePEc:eee:reensy:v:132:y:2014:i:c:p:72-82
    DOI: 10.1016/j.ress.2014.06.014
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    References listed on IDEAS

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    1. Panchenko, Valentyn, 2005. "Goodness-of-fit test for copulas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 176-182.
    2. van der Weide, J.A.M. & Pandey, M.D. & van Noortwijk, J.M., 2010. "Discounted cost model for condition-based maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 236-246.
    3. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    4. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    5. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    6. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    7. Tinga, Tiedo, 2010. "Application of physical failure models to enable usage and load based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1061-1075.
    8. Huard, David & Evin, Guillaume & Favre, Anne-Catherine, 2006. "Bayesian copula selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 809-822, November.
    9. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

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    2. Jiang, Yan & Jiang, Jiuchun & Zhang, Caiping & Zhang, Weige & Gao, Yang & Mi, Chris, 2019. "A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation," Energy, Elsevier, vol. 189(C).
    3. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan, 2017. "A newly formulated resilience measure that considers false alarms," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 417-427.
    4. Li, Zhixiong & Wu, Dazhong & Hu, Chao & Terpenny, Janis, 2019. "An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 110-122.
    5. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
    7. Xi, Zhimin & Zhao, Xiangxue, 2019. "An enhanced copula-based method for data-driven prognostics considering insufficient training units," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 181-194.
    8. Malinowski, Simon & Chebel-Morello, Brigitte & Zerhouni, Noureddine, 2015. "Remaining useful life estimation based on discriminating shapelet extraction," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 279-288.
    9. Xu, Yanwen & Renteria, Anabel & Wang, Pingfeng, 2022. "Adaptive surrogate models with partially observed information," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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