Groth, Katrina M., Swiler, Laura P. Use of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment (Technical Report) Sandia National Laboratories Albuquerque, NM, (SAND2013-2019), 2013.

BibTeX

@techreport{SAND2013-2019,
title = {Use of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment},
author = {Katrina M Groth and Laura P Swiler},
year = {2013},
date = {2013-03-01},
number = {SAND2013-2019},
address = {Albuquerque, NM},
institution = {Sandia National Laboratories},
abstract = {Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation & control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework.

This report describes the results of an early career LDRD project titled “Use of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment”. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.},
keywords = {Bayesian methods, Bayesian Networks, Human Reliability Analysis (HRA), Probabilistic risk assessment (PRA), uncertainty},
pubstate = {published},
tppubtype = {techreport}
}

Abstract

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation & control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework.

This report describes the results of an early career LDRD project titled “Use of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment”. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.