This thesis develops a new approach to fault tree analysis, namely the Binary Decision
Diagram (BDD) method. Conventional qualitative fault tree analysis techniques such
as the "top-down" or "bottom-up" approaches are now so well developed that further
refinement is unlikely to result in vast improvements in terms of their computational
capability. The BDD method has exhibited potential gains to be made in terms of
speed and efficiency in determining the minimal cut sets. Further, the nature of the
binary decision diagram is such that it is more suited to Boolean manipulation. The
BDD method has been programmed and successfully applied to a number of
benchmark fault trees.
The analysis capabilities of the technique have been extended such that all quantitative
fault tree top event parameters, which can be determined by conventional Kinetic Tree
Theory, can now be derived directly from the BDD. Parameters such as the top event
probability, frequency of occurrence and expected number of occurrences can be
calculated exactly using this method, removing the need for the approximations
Thus the BDD method is proven to have advantages in terms of both accuracy and
efficiency. Initiator/enabler event analysis and importance measures have been
incorporated to extend this method into a full analysis procedure.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.