Efficient basic event ordering schemes for fault tree analysis

Fault Tree Analysis is a commonly used means of assessing the system reliability performance in terms of its component’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.