Abstract
The Bayesian Network (BN) is a technique that utilizes updating, adapting and discrete-time-based analysis properties for system reliability analysis. Although the BN is a powerful technique, it still faces the challenge of modelling non-linear complex correlations of process components. This paper presents a Copula Bayesian Network (CBN) model to address challenge of modeling non-linear relationships. The superiority of the CBN model lies in integrating the advantage of Copula functions in modelling complex dependent structures with the cause-effect relationship reasoning of process variables using BN. Application of the CBN model is illustrated through a detailed reliability analysis of an example mud pump system. The results reveal the influence of different types of Copula functions and different parameters on the system reliability.