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research-article

Using simulator data to facilitate human reliability analysis

[+] Author and Article Information
Mashrura Musharraf

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John’s, Newfoundland and Labrador, Canada A1B 3X5
mashrura.musharraf@mun.ca

Allison Moyle

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John’s, Newfoundland and Labrador, Canada A1B 3X5
p13dabm@mun.ca

Faisal Khan

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John’s, Newfoundland and Labrador, Canada A1B 3X5
fikhan@mun.ca

Brian Veitch

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St John’s, Newfoundland and Labrador, Canada A1B 3X5
bveitch@mun.ca

1Corresponding author.

ASME doi:10.1115/1.4042538 History: Received September 05, 2018; Revised January 05, 2019

Abstract

Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained non-informative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment is used to update the priors and obtain informed posteriors. Use of the informed posteriors enables better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

Copyright (c) 2019 by ASME
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