Abstract
The paper deals with the problem of test frequency selection for multi-frequency parametric fault diagnosis of analog linear circuits. An appropriate set of test frequencies is determined by minimizing the conditionality of the sensitivity matrix based on the system of fault equations using a global stochastic optimization. A novel method based on the Particle Swarm Optimization, which provides more accurate results and improves the convergence rate, is described. The paper provides several practical examples of its application to test frequency selection for active RC filters. A comparison of the results obtained by the proposed method and by the Genetic Algorithm is also presented.
Highlights
Hk(jωk,i, p) = Mk,i, (2)Nowadays, continuing miniaturization of modern electronic devices leads to more complex circuits and systems
To reduce test costs a development of new robust methods for Automatic Test Plan Generation (ATPG) for analog circuits is one of the main objective in this area [1]
Analog faults can be classified into several classes, e.g. manufacturing tolerances, soft, hard and catastrophic faults, depending on the deviation of network parameters from their nominal values [2]
Summary
Nowadays, continuing miniaturization of modern electronic devices leads to more complex circuits and systems. In the case of one test point, the solvability is given as the rank of the Jacobian matrix associated with the system Eq (2): actual ones, the measurement of weak signals, e.g. in stop bands of analog filters, as well as the measurement at high frequencies is usually more problematic. All these errors can be minimized by an appropriately chosen set of test frequencies [3]. The testability degree of a circuit is independent of the fault localization method, the nominal values of network parameters and the selected set of test frequencies [5]. The ill-conditioned system of equations is prone to large numerical errors of the solution, in the case of the uncertainty of fixed (untested) network parameters, the final solution is mathematically correct but the estimated values of tested network parameters may not correspond to their
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