Fault Diagnosis of RC-coupled Amplifier Using Slope Fault Feature and Comparision with Different Neural Networks

This paper describe fault diagnosis of RC-Coupled amplifier using slope fault feature. These slope fault feature technique utilized to construct the fault dictionary for RC-Coupled amplifier. This fault dictionary used to generate different fault diagnosis model for analog circuit using artificial n...

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Veröffentlicht in:	2015 Fifth International Conference on Communication Systems and Network Technologies S. 1163 - 1166
Hauptverfasser:	Gupta, Shashank Kumar, Ayub, Shahanaz, Saini, J. P.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2015
Schlagworte:	Analog circuits Circuit faults Dictionaries Fault diagnosis feed forward backpropagation algorithm Feeds Neural networks perceptron neural network radial basis function neural network RC-Coupled amplifier Resistance slope fault feature voltage relation function
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Zusammenfassung:	This paper describe fault diagnosis of RC-Coupled amplifier using slope fault feature. These slope fault feature technique utilized to construct the fault dictionary for RC-Coupled amplifier. This fault dictionary used to generate different fault diagnosis model for analog circuit using artificial neural network technique. For generate the fault model three different type neural networks utilized. These neural networks are radial basis function neural network, perceptron neural network and feed forward back propagation algorithm neural network. In theses network radial basis function neural network shows 100 percentage efficiency, perceptron neural network shows 87.5 percentage efficiency and feed forward back propagation algorithm shows 99.31 percentage efficiency in the training and testing for fault dictionary.
DOI:	10.1109/CSNT.2015.75