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Reliability analysis for complex system with multi-source data integration and multi-level data transmission

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  • Jia, Xiang
  • Guo, Bo

Abstract

The Bayesian theory is appealing for reliability analysis of complex system using the data incorporation. However, there are still gaps among existing methods, such as that the common method is applicable to two-level system, multi-source data for components are not considered, k-out-of-n and standby structures are not studied with series and parallel simultaneously, the reliability is usually estimated as discrete variable, etc. For these problems, a Bayesian-based method is proposed on system under multiple levels. First, multi-source data for each target are integrated in lower level by Bayesian theory to derive the posterior distribution for reliability. Next, they are transmitted to higher level through the deterministic function concerning reliability depending on system structure. Further, the transmitted data from lower level are transformed to induced prior distribution for parameters in distribution of lifetime and integrated with native data in higher level to obtain the posterior for reliability. Finally, the reliability and remaining useful lifetime with respect to times in system-level are presented after the successive information propagation. An illustrative example is given to show the application of proposed method. Together with the sensitivity analysis, it proves that this method is feasible, practical and robust.

Suggested Citation

  • Jia, Xiang & Guo, Bo, 2022. "Reliability analysis for complex system with multi-source data integration and multi-level data transmission," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021005536
    DOI: 10.1016/j.ress.2021.108050
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    as
    1. Jiang, R. & Murthy, D.N.P., 2011. "A study of Weibull shape parameter: Properties and significance," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1619-1626.
    2. Guo, Jian & (Steven) Li, Zhaojun & (Judy) Jin, Jionghua, 2018. "System reliability assessment with multilevel information using the Bayesian melding method," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 146-158.
    3. Castet, Jean-Francois & Saleh, Joseph H., 2010. "Beyond reliability, multi-state failure analysis of satellite subsystems: A statistical approach," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 311-322.
    4. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xiaoqian, 2020. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part I – Independent systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    6. Wang, Yuhao & Pang, Yutian & Chen, Oliver & Iyer, Hari N. & Dutta, Parikshit & Menon, P.K. & Liu, Yongming, 2021. "Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    8. Ming Han, 2017. "The E-Bayesian and hierarchical Bayesian estimations for the system reliability parameter," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1606-1620, February.
    9. Jia, Xiang & Nadarajah, Saralees & Guo, Bo, 2018. "The effect of mis-specification on mean and selection between the Weibull and lognormal models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1875-1891.
    10. Wilson, Alyson G. & Huzurbazar, Aparna V., 2007. "Bayesian networks for multilevel system reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1413-1420.
    11. Mingyang Li & Qingpei Hu & Jian Liu, 2014. "Proportional hazard modeling for hierarchical systems with multi-level information aggregation," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 149-163.
    12. Yu, Wennian & Tu, Wenbing & Kim, Il Yong & Mechefske, Chris, 2021. "A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    13. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xianqi, 2019. "Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 123-142.
    14. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    15. 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.
    16. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    17. 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.
    18. Almalki, Saad J. & Nadarajah, Saralees, 2014. "Modifications of the Weibull distribution: A review," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 32-55.
    19. Yan, Tao & Lei, Yaguo & Li, Naipeng & Wang, Biao & Wang, Wenting, 2021. "Degradation modeling and remaining useful life prediction for dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    20. Wu, Chia-Huang & Yen, Tseng-Chang & Wang, Kuo-Hsiung, 2021. "Availability and Comparison of Four Retrial Systems with Imperfect Coverage and General Repair Times," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    21. Castet, Jean-Francois & Saleh, Joseph H., 2009. "Satellite and satellite subsystems reliability: Statistical data analysis and modeling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1718-1728.
    22. Lu, Biao & Chen, Zhen & Zhao, Xufeng, 2021. "Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    23. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    24. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    25. Jia, Xiang & Wang, Dong & Jiang, Ping & Guo, Bo, 2016. "Inference on the reliability of Weibull distribution with multiply Type-I censored data," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 171-181.
    26. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
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