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Title: Efficient basic event orderings for binary decision diagrams
Authors: Andrews, J.D.
Bartlett, L.M.
Keywords: Fault tree analysis
Binary Decision Diagrams
Issue Date: 1998
Publisher: © Institute of Electrical and Electronics Engineers (IEEE)
Citation: ANDREWS, J.D. and BARTLETT, L.M., 1998. Efficient basic event orderings for binary decision diagrams. IN: Annual Reliability and Maintainability Symposium. 1998 Proceedings. International Symposium on Product Quality and Integrity, Anahiem, Los Angeles, 19-22 January, pp. 61-68 [DOI: 10.1109/RAMS.1998.653583]
Abstract: Over the last five years significant advances have been made in methodologies to analyse the fault tree diagram. The most successful of these developments has been the Binary Decision Diagram (BDD) approach. The Binary Decision Diagram approach has been shown to improve both the efficiency of determining the minimal cut sets of the fault tree ancl also the accuracy of the calculation procedure used to determine the top event parameters. The BDD technique povides a potential alternative to the traditional approaches based on Kinetic Tree Theory. To utilise the Binary Decision Diagram approach the fault tree structure is first converted to the BDD format. This conversion can be accomplished efficiently but requires the basic events in the fault tree to be placed in an ordering. A poor ordering can result in a Binary Decision Diagram which is not an efficient representation of the fault tree logic structure. The advantages to be gained by utilising the BDD technique rely on the efficiency of the ordering scheme. Alternative ordering schemes have been investigated and no one scheme is appropriate for every tree structure. Research to date has not found any rule based means of determining the best way of ordering basic events for a given fault tree structure. The work presented in this paper takes a machine learning approach based on Genetic Algorithms to select the most appropriate ordering scheme. Features which describe a fault tree structure have been identified and these provide the inputs to the machine learning algorithm. A set of possible ordering schemes has been selected based on previous heuristic work. The objective of the work detailed in the pap:r is to predict the most efficient of the possible ordering alternatives from parameters which describe a fault tree structure.
Description: This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
URI: https://dspace.lboro.ac.uk/2134/3612
ISBN: 078034362X
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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