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New Progressive Variable Ordering for Binary Decision Diagram Analysis of Fault Trees
Author(s) -
Bartlett L. M.,
Du S.
Publication year - 2005
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.674
Subject(s) - binary decision diagram , heuristics , fault tree analysis , heuristic , variable (mathematics) , computer science , algorithm , boolean function , binary tree , fault (geology) , fault coverage , diagram , mathematics , reliability engineering , statistics , mathematical optimization , engineering , mathematical analysis , seismology , geology , electronic circuit , electrical engineering
The binary decision diagram (BDD) is the most efficient method currently available to analyse failure modes represented by fault trees. The fault tree is converted to this alternative structure representative of the failure mode as a Boolean equation. For the conversion the basic event variables within the fault tree are required to be placed in an order. The size of the resulting BDD and therefore the efficiency of the whole methodology is dependent upon the variable ordering chosen. Most commonly the order of variables is determined prior to the conversion using a structured or weighted approach and remains fixed during the process. Although there are several ordering heuristics available, no one heuristic has been found that will guarantee a minimal BDD for all fault trees. This paper proposes a new ordering methodology which seeks to select variables during the conversion process from a fault tree, allowing different potential ordering permutations on each path of the diagram. This method is simple to implement and is applied directly to the fault tree structure. When compared against the best sized BDD produced from 11 different methodologies, it produced a BDD of equal or smaller size in 82% of test cases. In addition, the technique has shown a 34% increase in the likelihood of producing the best BDD compared with the best individual heuristic from the 11 tested. Copyright © 2005 John Wiley & Sons, Ltd.