Uncertainty Quantification in Fault Tree Analysis: Estimating Business Interruption due to Seismic Hazard

Abstract

This paper presents an approach based on fault tree analysis and subset simulation for quantifying uncertainty in the risk assessment of complex industrial facilities. Downtime estimation of industrial facilities after an extreme event is critical for risk valuation, as business interruption contributes significantly to monetary losses. Industrial facilities are complex systems with many critical, interdependent components. Such facilities are thus amenable to modeling using fault trees. Fault tree analysis breaks down a facility’s layout into system components and links component failure probabilities through Boolean logic to estimate the larger system’s failure probability. In estimating system failure probability, the lack of knowledge about failure probabilities of individual components introduces uncertainty. Subset simulation offers an efficient approach for propagating these component-level uncertainties to the system level. However, when parameters are highly correlated, traditional algorithms used in subset simulation may suffer from low acceptance rates (ratio of new samples to total samples), resulting in repeated samples, thereby compromising efficiency. This paper demonstrates that the proposed treatment allows application of subset simulation to uncertainty quantification of large fault trees using a case study of a coal-fired power plant.