Bridging the simulator gap: Measuring motivational bias in digital nuclear power plant environments. (September 2018)

• Record Type: Journal Article
• Authors: Shirley, Rachel Benish; Smidts, Carol
• Is Part Of: Reliability engineering & system safety. Volume 177(2018)
• Page Start: 191
• Page End: 209
• Publication Date: 2018-09
• Subjects: Nuclear power -- Human reliability analysis -- Simulator studies -- Digital simulator environments -- Structural equation modeling -- Latent variables -- Mediation -- Sample size; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.04.016
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Environmental/Motivational biases in Nuclear Power Plant simulators are identified. A method for modeling effects of a simulator on operator performance is proposed. A demonstration experiment using student operators captures bias data. Quantitative Structural Equation Models of simulator bias are evaluated. Sample size for full scale experiments to measure simulator bias is estimated. Abstract: Using digital NPP simulator facilities for research in Human Reliability Analysis (HRA) and nuclear power research is increasingly popular. We propose a method for characterizing and quantifying the gap between data collected in a simulator and data that reflect NPP operations. Using novice operators, we demonstrate how to manipulate and measure the impact of the simulator environment on operator actions. A set of biases are proposed to characterize factors introduced by the simulator environment. There are two categories of simulator bias: Environmental Biases (physical differences between the simulator and the control room), and Motivational Biases (cognitive differences between training in a simulator and operating a NPP). This study examines Motivational Bias. A preliminary causal model of Motivational Biases is introduced and tested in a demonstration experiment using 30 student operators. Data from 41 simulator sessions are analyzed. Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. Quantitative models of the Motivational Biases using Structural Equation Modeling (SEM) are proposed. With these models, we estimate how the effects of the scenario conditions are mediated by simulator bias, and we demonstrate how to quantify the strength of these effects.