Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment
نویسندگان
چکیده
Most probabilistic risk assessment (PRA) and reliability methods commonly used at Johnson Space Center (JSC) make the assumption that component failures in a system are independent random occurrences. There are some exceptions (e.g. modeling common cause events), but because of the mathematical complications that occur when full dependency is assumed, it is not done by the standard models. This study investigates the use of models in which dependencies among the failure states has been considered via a variety of processes. Our study included: 1) analysis of a general block component diagram for path dependence and inter-arrival time correlations; 2) analysis of correlation among inter-arrival times on a small, generic event tree; 3)a semi-Markov approach designed to provide updated reliability predictions for general event trees.
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