A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

Groth, Katrina M.; Smith, Curtis L.; Swiler, Laura P.

doi:10.1016/j.ress.2014.03.010

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

Journal Article · Sat Apr 05 00:00:00 EDT 2014 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2014.03.010· OSTI ID:1072471

^[1]; Smith, Curtis L. ^[2]; Swiler, Laura P. ^[1]

Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States)

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1072471

Alternate ID(s):: OSTI ID: 1133869
OSTI ID: 22316492

Report Number(s):: SAND2013--2542J; PII: S0951832014000581

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Vol. 128; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

operator performance data
97 MATHEMATICS AND COMPUTING
Bayesian inference
Human Reliability Analysis (HRA)
nuclear power plant
simulator data

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

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