A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy-OR Gates

Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach.