Discrete Predictive Analysis in Probabilistic Safety Assessment

Abstract

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.

• Bayesian Prediction
• Binomial Distribution
• Calibration
• Common Cause Failure
• Mixture Distributions
• Poisson Distribution

Additional information

Notes on contributors

Paul H. Kvam

Dr. Kvam is an Associate Professor in the School of Industrial & Systems Engineering. His e-mail address is [email protected].

J. Glenn Miller

Mr. Miller is a Ph.D. student in the School of Industrial & Systems Engineering.