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Abstract

The paper presents a heuristic approach to the problem of analog circuit diagnosis. Different optimization techniques in the field of test point selection are discussed. Two new algorithms: SALTO and COSMO have been introduced. Both searching procedures have been implemented in a form of the expert system in PROLOG language. The proposed methodologies have been exemplified on benchmark circuits. The obtained results have been compared to the others achieved by different approaches in the field and the benefits of the proposed methodology have been emphasized. The inference engine of the heuristic algorithms has been presented and the expert system knowledge-base construction discussed.

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Authors and Affiliations

Andrzej Pułka
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Abstract

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.

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Authors and Affiliations

Yong Deng
Yibing Shi
Wei Zhang

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