Efficient basic event orderings for binary decision diagrams
AndrewsJ.D.
JacksonLisa
2008
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.