Is mapping a part of common cause failure quantification?

Abstract

Abstract Important issues in any procedure used for estimating basic event probabilities of common cause failures (CCF) for probabilistic safety assessments (PSA) are: which plants and systems to use, how to combine them, and how to transform data from systems with different numbers of similar components to obtain CCF-rates for a specific group of components. These issues are addressed with focus on the last part called “mapping”. Certain parametric models are considered for transforming CCF event experience from data-source plants to the target plant, the plant of interest. Two sets of rules are reviewed and compared for transforming rates and assessment uncertainties from larger to smaller systems i.e. mapping down. Epistemic uncertainties are taken into account in the estimation. 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, uncertainties and alpha factors from smaller to larger systems. These rules are not limited to a binomial CCF model. Consistency requirements are severe and dictate certain limits to possible parametric values. Empirical alpha factors are used to quantify robust mapping ratios of complete CCF-rates. Mapping is critically analyzed and practical recommendations are made.