ANN application in electronic diagnosis-preliminary results

In this paper artificial neural networks (ANNs) are applied to diagnosis of catastrophic defects in a linear analog circuit. In fact, today the technical diagnosis is great challenge for design engineers because the diagnostic problem is generally underdeterminate. It is also a deductive process with one set of data creating, in general, unlimited number of hypotheses among which one should try to find the solution. So, the diagnosis methods are mostly based on proprietary knowledge and personal experience, although they were built into integrated diagnostic equipment. ANN approach is proposed here as an alternative to existing solutions, based on the fact that ANNs are expected to encompass all phases of the diagnostic process: symptom detection, hypothesis generation, and hypothesis discrimination. The approach is demonstrated on the example of a simple resistive electrical circuit, and the generalization property is shown by supplying noisy data to ANNs inputs during diagnosis.