Monte Carlo simulation of complex system mission reliability

E. E. Lewis*, F. Boehm, C. Kirsch, B. P. Kelkhoff

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


A Monte Carlo methodology for the reliability simulation of highly redundant systems is presented. Two forms of importance sampling, forced transitions and failure biasing, allow large sets of continuous-time Markov equations to be simulated effectively and the results to be plotted as continuous functions of time. A modification of the sampling technique also allows the simulation of both nonhomogeneous Markov processes and of non-Markovian processes involving the replacement of worn parts. A number of benchmark problems are examined. For problems with large numbers of components, Monte Carlo is found to result in decreases in computing times by as much as a factor of 20 from the Runge-Kutta Markov solver employed in the NASA code HARP.

Original languageEnglish (US)
Pages (from-to)497-504
Number of pages8
JournalWinter Simulation Conference Proceedings
StatePublished - 1989
Event1989 Winter Simulation Conference Proceedings - WSC '89 - Washington, DC, USA
Duration: Dec 4 1989Dec 6 1989

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety
  • Applied Mathematics


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