BibTeX

@inproceedings{Liao2013,
title = {Leveraging Existing Human Performance Data for Quantifying the IDHEAS HRA Method},
author = {H Liao and K Groth and S Stevens-Adams and J Xing},
year = {2013},
date = {2013-09-01},
booktitle = {Proceedings of the European Society for Reliability Annual Meeting (ESREL 2013)},
address = {Amsterdam},
abstract = {The Integrated Decision-tree Human Event Analysis System (IDHEAS) was developed as a new Human Reliability Analysis (HRA) method to reduce HRA variability and improve estimates of human error probabilities (HEPs) (NRC, 2012). Based on cognitive models and mechanisms underlying human behavior, the method employs a framework of 14 crew failure modes (CFMs) to represent human failures that are typically found in nuclear power plants. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts with each path in the DT representing a different con-text. This article documents a study for collecting and using available human performance data and relevant information to inform HEP estimates as part of the efforts of developing the quantification model described above. The data needs for the quantification model are first discussed, and the technical challenges in collect-ing and using such data are then presented.},
keywords = {human error, Human Reliability Analysis (HRA), nuclear power, Performance Shaping Factors (PSFs)},
pubstate = {published},
tppubtype = {inproceedings}
}

Abstract

The Integrated Decision-tree Human Event Analysis System (IDHEAS) was developed as a new Human Reliability Analysis (HRA) method to reduce HRA variability and improve estimates of human error probabilities (HEPs) (NRC, 2012). Based on cognitive models and mechanisms underlying human behavior, the method employs a framework of 14 crew failure modes (CFMs) to represent human failures that are typically found in nuclear power plants. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts with each path in the DT representing a different con-text. This article documents a study for collecting and using available human performance data and relevant information to inform HEP estimates as part of the efforts of developing the quantification model described above. The data needs for the quantification model are first discussed, and the technical challenges in collect-ing and using such data are then presented.