Application of heuristic search and information theory to sequential fault diagnosis

The problem of constructing optimal and near-optimal test sequences to diagnose permanent faults in electronic and electromechanical systems is considered. The test sequencing problem is formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be NP-complete. The approach used is based on integrated concepts from information theory and heuristic AND/OR graph search methods to subdue the computational explosion of the optimal test-sequencing problem. Lower bounds on the optimal cost-to-go from the information-theoretic concepts of Huffman coding and entropy are derived. These lower bounds ensure that an optimal solution is found using the heuristic AND/OR graph search algorithms; they have made it possible to obtain optimal test sequences to problems that are intractable with traditional dynamic programming techniques. In addition, a class of test-sequencing algorithms that provide a tradeoff between solution quality and complexity have been derived using the ε-optimal and limited search strategies