In this paper we present a technique for analog fault diagnosis (AH)) based on an evolutionary algorithm. The nonlinear DC circuit equations are written with modified nodal analysis (MNA). Parametric fault models and/or fault compensation models are implemented for linear circuit elements and for common nonlinear devices (diodes, BJTs, MOSFETs, OPAMPs). The diagnosis is performed by solving a nonlinear program (NLP) built with the circuit equations, with the measurements taken in the circuit and with the tolerances of the circuit elements and devices. This NI2P is solved by minimizing a function of the fault variables and of the unknown circuit variables, with an evolutionary algorithm. The population in the evolutionary algorithm consists of sub-populations (with one or more individuals) each of them related to a specific fault. The technique was implemented with a suite of Per 1 programs and was applied to several examples. Some conclusions on the effectiveness and robustness of the evolutionary algorithm to perform analog fault diagnosis are presented.
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