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An improved decomposition scheme for assessing the reliability of embedded systems by using dynamic fault trees

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  • Huang, Chin-Yu
  • Chang, Yung-Ruei

Abstract

The theories of fault trees have been used for many years because they can easily provide a concise representation of failure behavior of general non-repairable fault tolerant systems. But the defect of traditional fault trees is lack of accuracy when modeling dynamic failure behavior of certain systems with fault-recovery process. A solution to this problem is called behavioral decomposition. A system will be divided into several dynamic or static modules, and each module can be further analyzed using binary decision diagram (BDD) or Markov chains separately. In this paper, we will show a very useful decomposition scheme that independent subtrees of a dynamic module are detected and solved hierarchically. Experimental results show that the proposed method could result in significant saving of computation time without losing unacceptable accuracy. Besides, we also present an analyzing software toolkit: DyFA (dynamic fault-trees analyzer) which implements the proposed methodology.

Suggested Citation

  • Huang, Chin-Yu & Chang, Yung-Ruei, 2007. "An improved decomposition scheme for assessing the reliability of embedded systems by using dynamic fault trees," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1403-1412.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:10:p:1403-1412
    DOI: 10.1016/j.ress.2006.09.008
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    Citations

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    Cited by:

    1. Lindhe, Andreas & Norberg, Tommy & Rosén, Lars, 2012. "Approximate dynamic fault tree calculations for modelling water supply risks," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 61-71.
    2. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    3. Zixian, Liu & Xin, Ni & Yiliu, Liu & Qinglu, Song & Yukun, Wang, 2011. "Gastric esophageal surgery risk analysis with a fault tree and Markov integrated model," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1591-1600.
    4. Yan-Feng Li & Jinhua Mi & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers," Journal of Risk and Reliability, , vol. 229(6), pages 530-541, December.
    5. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2017. "Modeling reliability of power systems substations by using stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 13-22.
    6. Sejin Baek & Gyunyoung Heo, 2021. "Application of Dynamic Fault Tree Analysis to Prioritize Electric Power Systems in Nuclear Power Plants," Energies, MDPI, vol. 14(14), pages 1-17, July.
    7. Mohammad Nadjafi & Mohammad Ali Farsi & Hossein Jabbari, 2017. "Reliability analysis of multi-state emergency detection system using simulation approach based on fuzzy failure rate," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 532-541, September.
    8. Tyrväinen, T., 2013. "Risk importance measures in the dynamic flowgraph methodology," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 35-50.
    9. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.

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