Model Event/Fault Trees With Dynamic Uncertain Causality Graph for Better Probabilistic Safety Assessment

Abstract

Probabilistic safety assessment (PSA) has been widely applied to large complex industrial systems like nuclear power plants, chemical plants, etc. Event trees (ET) and fault trees (FT) are the major tools, but dependences and logic cycles may exist among and within them, and are not well addressed, leading to even optimistic estimates. Repeated representations and calculations exist. Causalities are assumed deterministic, while sometimes they are uncertain. This paper applies dynamic uncertain causality graph (DUCG) in PSA to overcome these problems. DUCG is a newly presented approach for uncertain causality representation and probabilistic reasoning, and has been successfully applied to online fault diagnoses of large complex industrial systems. This paper suggests to model all ETs and FTs of a target system as a single DUCG allowing uncertain causalities and avoiding repeated representations, and calculate the probabilities/frequencies of the undesired events by using the DUCG algorithm. In the calculation, the problems of dependencies and circular loops are solved. The suggested DUCG representation mode and calculation algorithm are presented and illustrated with examples. The results reveal the effectiveness and feasibility of this methodology.