Aviation Risk Analysis: U-bowtie Model Based on Chance Theory - Xiaolin Wang; Jihui Xu; Mingfa Zheng; Lei Zhang (IEEE Access, 2019)
Risk analysis is a crucial component in any aviation application. The conventional risk analysis schemes for systems encompassing large amounts of data are based on the probability theory. However, in some practical systems, the components of complex systems may have few or even zero samples, in which case the risk cannot be evaluated simply via statistics. This paper proposes a novel aviation risk analysis scheme based on the chance theory, which is a generation of both probability theory and uncertainty theory. In this paper, a U-bowtie model is first established according to the graphic structure and step-wise analysis process. A risk belief metric is utilized, and the risk variable is modified by a logic algorithm based on the operational law of chance theory. Then, several risk belief formulas are built for different respective system configurations; the risk of output events in the U-bowtie model can be calculated via the corresponding formulas. Additionally, the most contributed basic event and the most effective measurement can be obtained through sensitivity analysis. The effectiveness of the proposed scheme is validated by a case study. The results presented here may represent an innovative approach to managing risk in larger classes of complex systems.