Efficient basic event ordering schemes for fault tree analysis
JacksonLisa
AndrewsJ.D.
2006
Fault Tree Analysis is a commonly used means of assessing the system reliability
performance in terms of its componentâ€™s reliability characteristics. More recently,
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
and also the accuracy of the calculation procedure used to determine the top event
parameters.
To utilize the Binary Decision Diagram approach the fault tree structure is first
converted to the BDD format. Implementing the conversion of the tree is relatively
straight forward but requires the basic events of the tree to be placed in an ordering.
The ordering scheme chosen is critical to the size of the BDD produced, and hence the
advantages of this technique. Alternative ordering schemes have been investigated
and no one scheme is appropriate for every 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 paper is to predict
the most efficient of the possible ordering alternatives from parameters which
describe a fault tree structure.