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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 7657054, 9 pages
Research Article

Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2Department of Communication Engineering, Chengdu Technological University, Chengdu 611731, China

Received 21 May 2016; Revised 18 July 2016; Accepted 28 July 2016

Academic Editor: Rodolfo Zunino

Copyright © 2016 Jian Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.