Extended accident scenario modeling based on Bayesian networks for risk evaluation

Conventional risk evaluation technique based on accident scenario such as event tree/fault tree suffer severe limitations of handling event dependencies and uncertainty. These dependencies and uncertainty are cumbersome to take into account when using standard event tree/fault tree modeling due to its clumsy structure and complicated quantitative solution. To make the accident scenario model more realistic, a method is proposed to explicitly represent the failures cascading effect dependency and uncertainty using Bayesian networks (BN). A simplified example of spacecraft hydrazine leak accident taken from literature illustrates the ideas presented above, and concludes that BN is a superior technique to fit a wide variety of accident scenarios profiting from its flexible structure and powerful reasoning.