A new fault tree analysis approach based on imprecise reliability model

Fault tree analysis is a powerful and computationally efficient technique for safety analysis and reliability prediction. It decomposes an undesired failure into multiple possible root causes by constructing a sub-event tree and spreading it into basic events. Classical reliability theory using probability theory to quantify the uncertainties of basic events encounters many challenges when failure data are limited. In this case, uncertainty quantification should be carried out based on subjective information, such as experts’ assessment or engineers’ experience. As a generalization of probability theory, imprecise probability theory can quantify subjective information as the upper and lower expectations or previsions. In this article, a fault tree analysis algorithm incorporating subjective information into imprecise reliability models of basic events is proposed to calculate the failure interval of lubricating oil warning system.