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Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics

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  • Yang, Lechang
  • Wang, Pidong
  • Wang, Qiang
  • Bi, Sifeng
  • Peng, Rui
  • Behrensdorf, Jasper
  • Beer, Michael

Abstract

In practical engineering, the presence of dependent evidence is not rare due to various imperfections. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this paper, we propose a Bayesian reliability approach for complex systems with dependent life metrics. Notions such as explicit evidence and implicit evidence are established based on an identification of different roles of multiple dependent evidence in the likelihood construction. A likelihood decomposition method is developed to convert the overall likelihood into a product of Explicit Evidence-based Likelihood (EEL) function and Implicit Evidence-based Likelihood (IEL) function. An inferential diagram is developed to intuitively generate the required implicit evidence taking both outer-source information and the system configuration into consideration. An algorithm is then presented for implementation. The contribution of our work is a systematic investigation of the role of dependent evidence in system reliability evaluation and a full Bayesian approach that is applied to various system reliability models. Extensive numerical cases and a practical engineering case are demonstrated for validation and to illustrate the benefits of our approach.

Suggested Citation

  • Yang, Lechang & Wang, Pidong & Wang, Qiang & Bi, Sifeng & Peng, Rui & Behrensdorf, Jasper & Beer, Michael, 2021. "Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000375
    DOI: 10.1016/j.ress.2021.107469
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    References listed on IDEAS

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    1. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
    2. Insua, David Rios & Ruggeri, Fabrizio & Soyer, Refik & Wilson, Simon, 2020. "Advances in Bayesian decision making in reliability," European Journal of Operational Research, Elsevier, vol. 282(1), pages 1-18.
    3. Feng, Geng & Patelli, Edoardo & Beer, Michael & Coolen, Frank P.A., 2016. "Imprecise system reliability and component importance based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 116-125.
    4. Jackson, Chris & Mosleh, Ali, 2016. "Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 124-135.
    5. Junyu Guo & Hong-Zhong Huang & Weiwen Peng & Jie Zhou, 2019. "Bayesian information fusion for degradation analysis of deteriorating products with individual heterogeneity," Journal of Risk and Reliability, , vol. 233(4), pages 615-622, August.
    6. Graves, T.L. & Hamada, M.S. & Klamann, R. & Koehler, A. & Martz, H.F., 2007. "A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1476-1483.
    7. Jackson, Chris & Mosleh, Ali, 2012. "Bayesian inference with overlapping data for systems with continuous life metrics," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 217-231.
    8. Patelli, Edoardo & Feng, Geng & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2017. "Simulation methods for system reliability using the survival signature," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 327-337.
    9. Mingyang Li & Jiali Han & Jian Liu, 2017. "Bayesian nonparametric modeling of heterogeneous time-to-event data with an unknown number of sub-populations," IISE Transactions, Taylor & Francis Journals, vol. 49(5), pages 481-492, May.
    10. Louis J. M. Aslett & Frank P. A. Coolen & Simon P. Wilson, 2015. "Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature," Risk Analysis, John Wiley & Sons, vol. 35(9), pages 1640-1651, September.
    11. Graves, T.L. & Hamada, M.S. & Klamann, R.M. & Koehler, A.C. & Martz, H.F., 2008. "Using simultaneous higher-level and partial lower-level data in reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1273-1279.
    12. Yontay, Petek & Pan, Rong, 2016. "A computational Bayesian approach to dependency assessment in system reliability," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 104-114.
    13. Wilson, Alyson G. & Anderson-Cook, Christine M. & Huzurbazar, Aparna V., 2011. "A case study for quantifying system reliability and uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1076-1084.
    14. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
    15. Shi-Woei Lin & Yi-Ting Liu & Mohammad Adam Jerusalem, 2018. "Bayesian reliability analysis of a products of probabilities model for parallel systems with dependent components," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1521-1532, February.
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    6. Zhang, Wangxin & Han, Qiang & Shang, Wen-Long & Xu, Chengshun, 2024. "Seismic resilience assessment of interdependent urban transportation-electric power system under uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).

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