BARTLETT, L.M., 2004. Neural network selection mechanism for BDD construction, Quality and Reliability Engineering International, 20 (3) pp. 217-223.
The binary decision diagram (BDD) methodology is the latest approach used to
improve the analysis of the fault tree diagram, which gives a qualitative and
quantitative assessment of specified risks. To convert the fault tree into the necessary
BDD format requires the basic events of the tree to be placed in an ordering.
The ordering of the basic events is critical to the resulting size of the BDD,
and ultimately affects the performance and benefits of this technique. A number
of heuristic approaches have been developed to produce an optimal ordering
permutation for a specific tree, however they do not always yield a minimal BDD
structure for all trees. Latest research considers a neural network approach used to
select the ‘best’ ordering permutation from a given set of alternatives. To use this
approach characteristics are taken from the fault tree as guidelines to selection of
the appropriate ordering permutation. This paper looks at a new method of using the
Jacobian matrix to choose the most desired characteristics from the fault tree, which
will aid the neural network selection procedure.