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

Highlights

• 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.

Abstract

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.

Introduction

A comprehensive probabilistic risk assessment (PRA) is an essential element of safety and reliability assurance for many complex engineering systems. The aim of the PRA is to understand the possible failure scenarios, the corresponding adverse consequences, and the failure scenarios’ probabilities. Most engineering systems can be characterized as human-machine systems, in which the human operator and the technical system are interacting. For that reason it is essential for a PRA to consider not only failures of technical components but also the effect of human actions and human inaction. Human reliability analysis (HRA) models human elements as part of PRAs; in general through identification and quantification of human failure events (HFEs) in PRA models. A variety of methods have been developed and applied in this field to determine human error probabilities (HEPs) corresponding to HFEs. Among the most important representatives are THERP [40], SPAR-H [15] and ATHEANA [7].

The limitations of existing HRA methods have been widely discussed previously [14], [18], [20], [28], [37], [4], [44]. Two interrelated shortcomings in existing HRA methods are the limited scientific basis used to develop those methods and the use of simplified modeling techniques, which lack causal structure and quantitative traceability.

Ongoing research into human performance is addressing the first shortcoming. The scientific foundations for human reliability have been explored and documented in the work by Whaley et al. [43] on the psychological basis of HRA. In particular, they introduce a set of psychological failure mechanisms and proximate causes, which can lead to human failure events. Furthermore, they provide detailed insight into the factors that affect human performance (Performance Influencing Factors, PIFs), the dependency between those factors, and the causal pathways from those factors to human errors. International data collection activities offer insight into human performance in complex engineered systems [31], [6], [8], which provide new opportunities to improve the quantitative basis of HRA.

The second shortcoming, the lack of causal structure and quantitative traceability, is being addressed through advanced modeling efforts. Bayesian Network (BN) models (also called Bayesian Belief Networks), have becoming increasingly popular within HRA as a means for addressing these shortcomings because of their ability to explicitly model cause and effect combined with the ability to incorporate information from different sources [1], [27]. Ongoing international research has demonstrated the ability of BNs both to capture the causal relationships among PIFs and to facilitate quantification of those relationships [16], [29], [33], [39].

The psychological foundation has been leveraged in the development of two new HRA Methods, the IDHEAS (Integrated Decision-Tree Human Event Analysis System) method [45] and the PHOENIX method [10], [11]. Both IDHEAS and PHOENIX introduce the concept of crew failure modes (CFMs), a characterization of ways that a human failure event can occur during a crew interaction with the system. Both methods include a quantitative model relating PIFs to CFMs. However, the quantitative models in IDHEAS fall short of both causal and quantitative traceability; e.g. the motivation for the exclusion of cognitive mechanisms and PIFs from the method remains unclear [36]. The PHOENIX method uses a BN model for quantification, but there are no directed arcs from one PIF to another, and thus the causal paths from the cognitive literature are not fully captured.

In this paper we propose a methodology to expand the scientific basis and traceability of HRA by capturing causal paths from cognitive literature in BN models. Furthermore we present a method for quantifying the BN model using Bayesian parameter updating to combine human performance data with expert elicitation results.

We introduce the methodology by developing a Bayesian network (BN) model for a single CFM from the IDHEAS method. We illustrate the procedure step by step, starting from the corresponding IDHEAS decision tree model, expanding the CFM model to a level where its cognitive foundation is modeled explicitly, and finally reducing the expanded model to a level where its quantification becomes straightforward. This process enhances the traceability between the HRA quantification models and the underlying cognitive literature basis. In addition we provide a method to quantify the new model based on expert elicitation and then show how a database can be used to update these expert elicited distributions, such that the final model is based on both expert knowledge and observed data.