An algorithm for dividing ambiguity sets for analog fault dictionary

A new algorithm for dividing ambiguity sets based on the lowest error probability for analog fault dictionary is proposed. The problem of tolerance affecting diagnostic accuracy in analog circuits is discussed. A statistical approach is used to derive the probability distribution of the tolerances of the output signal characteristics both in the absence and in the presence of faults in the circuit. For example, in this paper, Monte Carlo technique has been applied for the analysis of tolerance. The lowest error probabilities are computed according to Bayesian strategy. Using the PSpice software package, a detailed simulation program was developed to implement the proposed technique. The simulation software was packaged and then integrated with a symbolic analysis program that divides the ambiguity sets and structure the software package for the analysis before testing in the fault dictionary. Furthermore, the proposed approach can be easily extended to select the testing nodes leading to the selection of optimized nodes for the analog fault diagnosis.