Critical comparison among some analog fault diagnosis procedures based on symbolic techniques

The parametric fault diagnosis techniques play an important part in the field of analog fault diagnosis. These techniques, starting from a series of measurements carried out on a previously selected test point set, given the circuit topology and the nominal values of the components, are aimed at determining the effective values of the circuit parameters by solving a set of equations nonlinear with respect to the component values. Here the role of symbolic techniques in the automation of parametric fault diagnosis of analog circuits is investigated. Three different methodologies were used by authors during last few years, based on Newton-Raphson type algorithms, neural networks and genetic algorithms, and a comparison has been made. In all the three cases the quite realistic hypothesis of bounded number of faulty components (k-fault hypothesis) is made, taking into account component tolerances. The results of the comparison are summarized.