This paper presents methods for predicting future numbers of component failures for probabilistic safety assessments (PSAs). The research is motivated and illustrated by discrete failure data from the nuclear industry, including failure counts for emergency diesel generators, pumps, and motor operated valves. Failure counts are modeled with Poisson and binomial distributions. Multiple-failure environments create extra problems for predictive inference, and are a primary focus of this paper. Common cause failures (CCFs), in particular, refer to the simultaneous failure of system components due to an external event. CCF prediction is investigated, and approximate inference methods are derived for various CCF models.
Copyright © 2002 American Society for Quality. This article first appeared in Journal of Quality Technology 34, no. 1 (January 2002): 106-17.
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Kvam, Paul H., and J. Glenn Miller. "Discrete Predictive Analysis in Probabilistic Safety Assessment." Journal of Quality Technology 34, no. 1 (January 2002): 106-117.