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Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

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Volume 22, Issue 2


A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

Chaolong Zhang
  • Corresponding author
  • Hefei University of Technology, School of Electrical Engineering and Automation, 230009 Hefei, China
  • Anqing Normal University, School of Physics and Electronic Engineering, 246011 Anqing, China
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/ Yigang He / Lei Zuo / Jinping Wang / Wei He
Published Online: 2015-06-03 | DOI: https://doi.org/10.1515/mms-2015-0025


Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

Keywords: analog circuits; incipient fault diagnosis; wavelet transform; kernel entropy component analysis; least squares support vector machine


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About the article

Received: 2014-11-11

Accepted: 2015-03-18

Published Online: 2015-06-03

Published in Print: 2015-06-01

Citation Information: Metrology and Measurement Systems, Volume 22, Issue 2, Pages 251–262, ISSN (Online) 2300-1941, DOI: https://doi.org/10.1515/mms-2015-0025.

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© Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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