BibTeX

@inproceedings{Zwirglmaier2015,
title = {Framework for a Bayesian Network Version of IDHEAS},
author = {K Zwirglmaier and D Straub and K M Groth},
year = {2015},
date = {2015-09-01},
booktitle = {Proceedings of the European Society for Reliability Annual Meeting (ESREL 2015)},
address = {Zurich, Switzerland},
abstract = {Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over the traditional HRA methods. We present a framework for developing a BN version of the IDHEAS (Integrated Decision-Tree Human Event Analysis System) HRA method, which is currently under development at the US NRC (Xing et al., 2013). The framework includes an extension of the IDHEAS graphical model to include additional causal paths and the use of BN node reduction algorithms to facilitate quantification of the model. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. Firstly, the developed extended BN structure illustrates the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, using node reduction algorithms allows the BN to be reduced to a level of detail at which quantification is as straightforward as the original approach in IDHEAS. We demonstrate the framework via the model for the crew failure mode ‘critical data misperceived’. We briefly discuss quantification of the model with a combination of expert-probabilities and information from databases like the SACADA database (Chang et al., 2014). This paper lays the foundations necessary to transform all of the IDHEAS crew failure models into a full IDHEAS-BN.},
keywords = {Bayesian methods, Bayesian Networks, causal models, human error, Human Reliability Analysis (HRA), nuclear power},
pubstate = {published},
tppubtype = {inproceedings}
}

Abstract

Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over the traditional HRA methods. We present a framework for developing a BN version of the IDHEAS (Integrated Decision-Tree Human Event Analysis System) HRA method, which is currently under development at the US NRC (Xing et al., 2013). The framework includes an extension of the IDHEAS graphical model to include additional causal paths and the use of BN node reduction algorithms to facilitate quantification of the model. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. Firstly, the developed extended BN structure illustrates the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, using node reduction algorithms allows the BN to be reduced to a level of detail at which quantification is as straightforward as the original approach in IDHEAS. We demonstrate the framework via the model for the crew failure mode ‘critical data misperceived’. We briefly discuss quantification of the model with a combination of expert-probabilities and information from databases like the SACADA database (Chang et al., 2014). This paper lays the foundations necessary to transform all of the IDHEAS crew failure models into a full IDHEAS-BN.