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

Journal: Computational intelligence and neuroscience
Published Date: Sep 8, 2016

Abstract

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.

Authors

  • Jian Xiong
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Communication Engineering, Chengdu Technological University, Chengdu 611731, China.
  • Shulin Tian
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Chenglin Yang
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

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