Consistent mapping of common cause failure rates and alpha factors

Abstract

The problem addressed is how to combine event experience data from multiple source plants to estimate common cause failure (CCF) rates for a target plant. Alternative models are considered for transforming CCF parameters from systems with different numbers of similar components to obtain CCF-rates for a specific group of components. Two sets of rules are reviewed and compared for transforming rates and assessment uncertainties from larger to smaller systems, i.e. mapping down. Mapping down equations are presented also for the alpha-factors and for the variances of CCF rates. Consistent rules are developed for mapping up CCF-rates and uncertainties from smaller to larger systems. These mapping up rules are not limited to a binomial CCF model. It is shown how consistency requirements set certain limits to possible parametric values. Empirical alpha factors are used to estimate robust mapping parameters, and mapping up equations are derived for alpha factors as well. An assessment uncertainty procedure is presented for treating incomplete or vague information when estimating CCF-rates. Numerical studies illustrate mapping rules and procedures. Recommendations are made for practical applications.