Using Simulator Data to Facilitate Human Reliability Analysis

Author:

Musharraf Mashrura1,Moyle Allison1,Khan Faisal1,Veitch Brian1

Affiliation:

1. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada e-mail:

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 noninformative 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 are 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.

Funder

Lloyd's Register Foundation

Publisher

ASME International

Subject

Mechanical Engineering,Ocean Engineering

Reference21 articles.

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2. Bridging the Gap Between HRA Research and HRA Practice: A Bayesian Network Version of SPAR-H;Reliab. Eng. Syst. Saf.,2013

3. Handbook of Human Reliability Analysis With Emphasis on Nuclear Power Plant Applications,1980

4. A Technique for Human Error Analysis (ATHEANA),1996

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