Capturing cognitive causal paths in human reliability analysis with Bayesian network models

• Published in: Reliability engineering & system safety 2017-02, Vol.158 (C), p.117-129
• Main Authors: Zwirglmaier, Kilian, Straub, Daniel, Groth, Katrina M.
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bayesian networks; Bayesian updating; Causal paths; Cognitive ability; Cognitive factors; Cognitive psychology; HRA; Mathematical models; Network reliability; Operator performance; Reliability; Reliability analysis; Reliability engineering
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2016.10.010

reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC [45]. We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. •A framework for building traceable BNs for HRA, based on cognitive causal paths.•A qualitative BN structure, directly showing these causal paths is developed.•Node reduction algorithms are used for making the BN structure quantifiable.•BN quantified through expert estimates and observed data (Bayesian updating).•The framework is illustrated for a crew failure mode of IDHEAS.