Self-organizing neural network for fault location in electrical circuits

Stanisław Osowski , Krzysztof Siwek

Abstract

A novel approach to fault location in analog dynamic circuits based on the application of self-organizing neural network has been presented. Important features are a very good generalization property and fast speed. Once the network has been trained, the recognition of the fault is done immediately, irrespective of the size of the circuit. The network is able to detect faults in the nonideal circuit, in which the tolerance of elements is taken into account. Two cases of analog circuits have been simulated and checked: the RLC circuit at multiple measurement points and the measurement done at external nodes of the circuit for multiple frequencies. The results of numerical experiments are given and discussed
Author Stanisław Osowski IETSIP
Stanisław Osowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Krzysztof Siwek IETSIP
Krzysztof Siwek,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
Pages265-268 vol.2
Book 1998 IEEE International Conference on Electronics, Circuits and Systems, vol. 2, 1998
Keywords in Englishanalog dynamic circuits, analogue circuits, biquadratic filters, circuit analysis computing, computer modelling, external nodes, fault location, fault recognition, generalisation (artificial intelligence), generalization property, Kohonen network, ladder networks, learning (artificial intelligence), multiple measurement points, nonideal circuit, RC active biquadratic filter, RC circuits, RLC ladder circuit, self-organising feature maps, self-organizing neural network, tolerance
DOIDOI:10.1109/ICECS.1998.814877
Score (nominal)3
Citation count*3 (2015-01-27)
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