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Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation

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  • Zhou, Daoqing
  • Sun, C.P.
  • Du, Yi-Mu
  • Guan, Xuefei

Abstract

The study presents a new method to model the degradation and reliability of multi-function complex systems using the hazard rate matrix and Markovian approximation. The system is hierarchically decomposed into a set of states according to the states of its functions, and the hazard rate matrix is proposed to describe the failure rates of the functions. By using Markovian approximation, the elements of the hazard rate matrix can be expressed as a set of dynamical equations involving the failure information about the functions. Consequently, the dynamical behavior of the degradation for the whole system and its functions can be determined using the observed failure data of those functions instead of the lifetime data of the whole system, eliminating the need for lifetime testing data of the whole system in statistical-based reliability assessment. The overall method is illustrated using an example problem, and is further applied to a power system degradation problem to demonstrate its usage in realistic engineering applications.

Suggested Citation

  • Zhou, Daoqing & Sun, C.P. & Du, Yi-Mu & Guan, Xuefei, 2022. "Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006529
    DOI: 10.1016/j.ress.2021.108166
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    References listed on IDEAS

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    1. Nguyen, H.D. & Gouno, E., 2020. "Bayesian inference for Common cause failure rate based on causal inference with missing data," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. He, Jingjing & Huang, Min & Wang, Wei & Wang, Shaohua & Guan, Xuefei, 2021. "An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    4. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
    5. Guo, Haitao & Yang, Xianhui, 2007. "A simple reliability block diagram method for safety integrity verification," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1267-1273.
    6. Kelly, Dana L. & Smith, Curtis L., 2009. "Bayesian inference in probabilistic risk assessment—The current state of the art," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 628-643.
    7. Jinhua Mi & Yan-Feng Li & Weiwen Peng & Hong-Zhong Huang, 2018. "Reliability Analysis of Complex Multi-state System with Common Cause Failure Based on DS Evidence Theory and Bayesian Network," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 19-38, Springer.
    8. Choe, Do-Eun & Gardoni, Paolo & Rosowsky, David & Haukaas, Terje, 2008. "Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 383-393.
    9. Lisnianski, Anatoly, 2007. "Extended block diagram method for a multi-state system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1601-1607.
    10. Dongjin Lee & Rong Pan, 2017. "Predictive maintenance of complex system with multi-level reliability structure," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4785-4801, August.
    11. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    12. Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
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