Comparison of Maximus/Bounding and Bayes/Monte Carlo for fault tree uncertainty analysis

Abstract

A comparison of two methodologies for the analysis of uncertainty in risk analyses is presented. One methodology combines approximate methods for confidence interval estimation of system reliability with a bounding approach for information derived from expert opinion. The other method employs Bayesian arguments to construct probability distributions for component reliabilities using data from experiments and observation and expert opinion. The system reliability distribution is then derived using a conventional Monte Carlo analysis. An extensive discussion of the differences between confidence intervals and Bayesian probability intervals precedes the comparison. The comparison is made using a trial problem from the Arkansas Nuclear One-Unit 1 Nuclear Power Plant. It is concluded that the Maximus/Bounding methodology tends to produce somewhat longer intervals than the Bayes/Monte Carlo method, although this finding is based on comparisons made under nonidentical assumptions regarding the treatment of operator recovery rates. The Bayes/Monte Carlo method is shown to produce useful information regarding the importance of uncertainty about each component's reliability in determining overall uncertainty. 8 refs., 31 figs., 5 tabs.

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