PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Vincent Paglioni Co-author(s): Torrey Mortenson (torrey.mortenson@inl.gov)
Katrina M. Groth (kgroth@umd.edu)
| The human failure event: what is it and what should it be? |
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| The human failure event (HFE) is typically the intersection point between the probabilistic risk assessment (PRA) of a complex engineering system (i.e., nuclear power plant (NPP)) and a separate human reliability analysis (HRA) methodology. In that lens, the HFE represents the total accounting of human error in the safety assessment of NPPs. The HFE is a pivotal aspect of any PRA. Identifying and quantifying the human error probability (HEP), which is the probability associated with an HFE, has typically been the focus of HRA methods in nuclear power. The HFE has typically been the intersection point between the parent PRA and its embedded HRA method. However, “HFE” has not been rigorously defined for either HRA or PRA, and so the entire field of risk analysis lacks a formal definition of what constitutes an HFE . HFE is, of course, a failure of some sort – but at what level of abstraction should the HFE be defined? Is the HFE simply the result of any failed task, or should it represent something larger in scope than a single task or even a set of tasks can define? In this paper, we discuss the need for rigorous definition as a subset of a much broader necessity in HRA for clarity around key terminology. We propose a framework for identifying and defining HFEs at a high level of abstraction. Recognizing that task failures are not the exceedingly rare phenomenon that the notion of a failure event might suggest, we propose that the idea of an HFE must be something more complicated than a single task failure. |
Paper PA192 Preview
Author and Presentation Info
Lead Author Name: Vincent Paglioni (paglioni@umd.edu)
Bio: Vincent is a Ph.D. candidate in Reliability Engineering at the University of Maryland. His research, advised by Dr. Katrina Groth, focuses on the conceptualization, modeling and quantification of dependency in human reliability analysis. Vincent's research is examining dependency through a basis in causality and uses causal Bayesian Networks as the architecture for modeling dependency relationships between HRA variables.
Country: United States of America
Company: University of Maryland, College Park
Job Title: Graduate Research Assistant
Bio: Vincent is a Ph.D. candidate in Reliability Engineering at the University of Maryland. His research, advised by Dr. Katrina Groth, focuses on the conceptualization, modeling and quantification of dependency in human reliability analysis. Vincent's research is examining dependency through a basis in causality and uses causal Bayesian Networks as the architecture for modeling dependency relationships between HRA variables.
Country: United States of America
Company: University of Maryland, College Park
Job Title: Graduate Research Assistant