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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Published in:2015 Fifth International Conference on Communication Systems and Network Technologies pp. 1163 - 1166
Main Authors: Gupta, Shashank Kumar, Ayub, Shahanaz, Saini, J. P.
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2015
Subjects:
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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Abstract 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.
AbstractList 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.
Author Ayub, Shahanaz
Saini, J. P.
Gupta, Shashank Kumar
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  givenname: Shahanaz
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  fullname: Saini, J. P.
  email: jps_uptu@rediffmail.com
  organization: Dept. of Electron. & Commun. Eng., Bundelkhand Inst. of Eng. & Technol., Jhansi, India
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Snippet This paper describe fault diagnosis of RC-Coupled amplifier using slope fault feature. These slope fault feature technique utilized to construct the fault...
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StartPage 1163
SubjectTerms 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
Title Fault Diagnosis of RC-coupled Amplifier Using Slope Fault Feature and Comparision with Different Neural Networks
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