Evidential network-based failure analysis for systems suffering common cause failure and model parameter uncertainty

The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or more components caused by physical interactions or shared environments, often exists in advanced engineered systems and computing systems. In this paper, by considering both the common cause failure and the epistemic uncertainty associated with model parameters, an evidential network model embedded with common cause failure is proposed to facilitate system failure analysis. The detailed transformations from some logic gates of a fault tree to an evidential network model are given. Moreover, the conditional belief mass tables are constructed to quantify the dependency between the states of components and the entire system. An engineering case of an aero-engine oil system, together with comparative results, is presented to demonstrate the effectiveness of the proposed evidential network model.