A multi-attribute decision based optimum test point selection for analog fault dictionary techniques

The optimal test point selection is an important problem in testability analysis and diagnosis. In this paper, a new algorithm based on graph-search and multi-attribute decision is proposed. Firstly, A* algorithm is used for graph-search, but when cost functions f(x) of two nodes are equal, three attributes describing a node are introduced, that is, information entropy, the number of un-isolated faults, the number of available test points for expanding. Secondly, a multi-attribute decision based on maximum deviation principle is used for nodes evaluation in order to select the best node for expanding. The proposed algorithm could overcome deviation brought by node evaluation based on information theory metrics only, which results in high accuracy. The outcome of simulation verification at the end of this paper manifests that this algorithm has excellent accuracy as the exhaustive algorithm, and is more quickly for large scale computation.