Estimating the quantitative relation between PSFs and HEPs from full-scope simulator data. (May 2018)

• Record Type: Journal Article
• Authors: Kim, Yochan; Park, Jinkyun; Jung, Wondea; Choi, Sun Yeong; Kim, Seunghwan
• Is Part Of: Reliability engineering & system safety. Volume 173(2018)
• Page Start: 12
• Page End: 22
• Publication Date: 2018-05
• Subjects: Full-scope simulator -- Human error probability -- Human reliability analysis -- HuREX framework -- Logistic regression -- Performance shaping factor; 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.2018.01.001
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The relations between the PSFs and HEPs were estimated from full-scope simulator data. Statistical techniques of logistic regression and variable selection were applied. The significant PSF variables were found and their multipliers were estimated. Abstract: For probabilistically assessing the risks of nuclear power plants, human reliability analyses (HRAs) have been conducted to systematically predict human error probabilities (HEPs) of significant tasks that might affect system safety. To improve quality in the HRA results, solid empirical evidence for quantitative relations between performance shaping factors (PSFs) and HEPs is required. For generating the empirical evidence, the HRA data including human reliability data and contextual data should be collected and the quantitative relations between PSFs and HEPs should be properly estimated. In this study, in order to validate the statistical estimation approach for the relation between PSFs and HEPs, the simulation records of operator training programs that were performed via full-scope simulators were collected and analyzed by the HuREX (Human Reliability data EXtraction) framework. The simulator data including more than 10, 000 data points are then statistically analyzed by a logistic regression analysis method. From the statistical process, the significant variables affecting human reliability are deduced, and the PSF multipliers for each significant variable are estimated. The potentials and challenges of the statistical approach are discussed from the obtained results.