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An integrated methodology for the dynamic performance and reliability evaluation of fault-tolerant systems

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  • Domínguez-García, Alejandro D.
  • Kassakian, John G.
  • Schindall, Joel E.
  • Zinchuk, Jeffrey J.

Abstract

We propose an integrated methodology for the reliability and dynamic performance analysis of fault-tolerant systems. This methodology uses a behavioral model of the system dynamics, similar to the ones used by control engineers to design the control system, but also incorporates artifacts to model the failure behavior of each component. These artifacts include component failure modes (and associated failure rates) and how those failure modes affect the dynamic behavior of the component. The methodology bases the system evaluation on the analysis of the dynamics of the different configurations the system can reach after component failures occur. For each of the possible system configurations, a performance evaluation of its dynamic behavior is carried out to check whether its properties, e.g., accuracy, overshoot, or settling time, which are called performance metrics, meet system requirements. Markov chains are used to model the stochastic process associated with the different configurations that a system can adopt when failures occur. This methodology not only enables an integrated framework for evaluating dynamic performance and reliability of fault-tolerant systems, but also enables a method for guiding the system design process, and further optimization. To illustrate the methodology, we present a case-study of a lateral-directional flight control system for a fighter aircraft.

Suggested Citation

  • Domínguez-García, Alejandro D. & Kassakian, John G. & Schindall, Joel E. & Zinchuk, Jeffrey J., 2008. "An integrated methodology for the dynamic performance and reliability evaluation of fault-tolerant systems," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1628-1649.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:11:p:1628-1649
    DOI: 10.1016/j.ress.2008.01.007
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    References listed on IDEAS

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    1. Dominguez-Garcia, Alejandro D. & Kassakian, John G. & Schindall, Joel E., 2006. "Reliability evaluation of the power supply of an electrical power net for safety-relevant applications," Reliability Engineering and System Safety, Elsevier, vol. 91(5), pages 505-514.
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    Cited by:

    1. Langeron, Y. & Grall, A. & Barros, A., 2015. "A modeling framework for deteriorating control system and predictive maintenance of actuators," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 22-36.
    2. Di Giandomenico, F. & Itria, M.L. & Masci, P. & Nostro, N., 2014. "Automated synthesis of dependable mediators for heterogeneous interoperable systems," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 220-232.
    3. Masoud Rabbani & Reza Yazdanparast & Mahdi Mobini, 2019. "An algorithm for performance evaluation of resilience engineering culture based on graph theory and matrix approach," 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. 10(2), pages 228-241, April.
    4. Yang, Xiaole & Sam Mannan, M., 2010. "The development and application of dynamic operational risk assessment in oil/gas and chemical process industry," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 806-815.
    5. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    6. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.

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