Fault Tree Analysis Based on Dynamic Uncertain Causality Graph

Abstract

Fault Tree Analysis (FTA) has been widely applied to large, complex industrial systems like nuclear power plants, chemical systems, and weapon systems. Events in classical FTA are assumed binary-state and s-independent but multi-state, dependencies and logic cycles may exist within FTs. Moreover, causalities in FTA are assumed deterministic, while sometimes they may be uncertain. This paper applies Dynamic Uncertain Causality Graph (DUCG) in FTA to overcome aforementioned issues. This paper shows that any FT can be mapped into a DUCG graph. And with DUCG representation model and algorithm, additional modeling and analytical power are obtained. Multi-value, dependencies, logic cycles, and non-deterministic causalities in FTA are solved. This paper also depicts how to calculate the importance measurement, predict failure, and diagnose fault. The results reveal the effectiveness and feasibility of this methodology.