Efficient Mincuts Identification for Phased-Mission Systems

Fault Tree is an important model for reliability and safety assessment. The analysis performed on a fault tree can be either qualitative or quantitative. Both types of analyses may involve identifying minimal cut sets (MCS) or mincuts, each of which is a minimal combination of basic events (component failures) whose occurrence causes the top event (system failure) to occur. Considerable research efforts have been expended in the identification of MCSs for single-phased systems and networks. However, only little work is available for phased-mission systems (PMSs) and the existing method involves a large number of redundant computations in the MCS identification for eliminating redundancies across generated cut sets. This article proposes an MCS identification method based on binary decision diagrams (BDD) generated from the PMS fault tree using the backward ordering. By examining cut sets encoded by the generated BDD model, we identify two kinds of redundancies (inclusion relation-based and intra-implication relation-based) that prevent a cut set from being an MCS. Correspondingly, two BDD operations are developed for eliminating these two kinds of redundancies, contributing to correct and efficient MCS identification. As demonstrated through experiments on a highly-reliable distributed computing infrastructure PMS, the proposed MCS identification method is more efficient than the existing method.