A modular approach for analyzing static and dynamic fault trees

Three commonly used analytical techniques for reliability evaluation are fault trees, binary decision diagrams (BDD) and Markov chains. Each of these techniques have advantages and disadvantages and the choice depends on the system being modeled. Fault trees have been found to be the most popular choice in terms of building an analytical model of a system. It provides a compact representation of the system and is easily understood by humans. However, fault trees lack the modeling power and solution time increases exponentially with the size of the system being modeled. In this paper, we present a new exciting hybrid approach, called the modular approach, for the efficient analysis of both static and dynamic fault trees. It provides a combination of BDD solution for static fault trees and Markov chain solution for dynamic fault trees coupled with the detection of independent subtrees. The algorithms used for modularization, integrating the results obtained from the separate solution of the independent modules (subtrees) and incorporating coverage modeling are discussed in detail in this paper. The modular approach is applied to an example system to demonstrate the potential of this research.