A new heuristics for the event ordering in binary decision diagram applied in fault tree analysis

Abstract

Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Since their introduction, binary decision diagrams proved to be a valuable tool for complete quantification of hard fault tree models. As is known, the size of the binary decision diagram representation is mainly determined by the quality of the selected fault tree event ordering scheme. Finding the optimal event ordering for binary decision diagram representation is a computationally intractable problem, for which reason heuristic approaches are applied to find reasonable good ordering schemes. The existing method for finding optimal ordering schemes related to special types of fan-in 2 read-once formulas is employed in our research to develop a new heuristic for fault tree. Various fault tree simplification methods are used for the sake of reducing fault tree model discrepancy from fan-in 2 read-once formulas. The reduced fault tree is traversed in a depth-first manner, as for every gate, the best ordering scheme is chosen from selected sets of input permutations. The quality of the final event ordering scheme is compared to orderings produced with depth-first left most heuristic on a set of fault tree models addressed in the literature as well as on a set of our hard models. Our method proves to be a useful heuristic for finding good static event ordering, and it compares favourably to the known heuristic based on a depth-first left most assignment approach.

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