PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Kanoko Nishiono Co-author(s): Marilia Ramos, marilia.ramos@ucla.edu
Yoshikane Hamaguchi, hamaguchi_yoshikane_b3n@nra.go.jp
Ali Mosleh, mosleh@ucla.edu
| Dependency Analysis within Human Failure Events for Nuclear Power Plant: Comparison between Phoenix and SPAR-H |
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| Robust and realistic Human Error Probability (HEP) estimation within Human Reliability Analysis (HRA) relies upon, among other factors, appropriate consideration of the dependency between human failure events (HFEs). The approach for assessing dependency varies throughout HRA methods. The reasoning and cognitive basis behind the different approaches for dependency, their quantitative rationale, and their impact on the HEP are still subject to investigation from the HRA community. This paper aims to discuss the characteristics of HRA methodology considering dependency through a comparison between two approaches: Phoenix, developed by the University of California, Los Angeles, and SPAR-H, developed by Idaho National Laboratories. The comparison of their qualitative frameworks will be performed through three elements: HRA variables, environmental factors considered, and causal modeling methods. Additionally, the following two elements will be discussed for comparing the quantitative analysis: dependency value estimation method and HEP estimation method considering dependency. SPAR-H and Phoenix include dependencies between HFEs. In SPAR-H, there are four factors to assess a dependency condition: Crew (same or different), Time (close in time or not close in time), location (same or different), and cues (additional or no additional). It determines the dependency level on five grades using a Dependency Condition Table. In Phoenix, the analyst selects several factors which may serve to influence the crew performance among eight factors (Performance Impact Factors - PIFs): Human System Interface, Procedures, Resources, Knowledge/Abilities, Team Effectiveness, Bias, Stress, and Task Load. Bayesian Belief Network models the causal model. The discussions of this paper aim to establish a foundation for a complete comparison between the approaches for dependency throughout an application within a Probabilistic Risk Assessment (PRA) scenario. Furthermore, our findings could be exploited for comparison between other HRA methods. |
Paper NI96 Preview
Author and Presentation Info
Lead Author Name: Kanoko Nishiono (nishiono_kanoko_yg4@nra.go.jp)
Bio: Kanoko NISHIONO research about Human Reliability Analysis and Dynamic HRA at Japan Nuclear Regulation Authority. In bachelor and master course, I worked on the environmental dynamics of Cs-137 and developing the internal dose estimation methods. I aim to be a researcher who can provide reliable risk assessment information to the public by utilizing the off-site knowledge I learned as a student and the on-site knowledge I developed in my work.
Country: Japan
Company: J-NRA
Job Title: Technical Official
Bio: Kanoko NISHIONO research about Human Reliability Analysis and Dynamic HRA at Japan Nuclear Regulation Authority. In bachelor and master course, I worked on the environmental dynamics of Cs-137 and developing the internal dose estimation methods. I aim to be a researcher who can provide reliable risk assessment information to the public by utilizing the off-site knowledge I learned as a student and the on-site knowledge I developed in my work.
Country: Japan
Company: J-NRA
Job Title: Technical Official