title: Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM.
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.affiliation: 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.
authors.forename: Jian
authors.identifier: 0000-0003-0883-8185
authors.initials: J
authors.lastname: Xiong
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Shulin
authors.initials: S
authors.lastname: Tian
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Chenglin
authors.identifier: 0000-0002-0834-5164
authors.initials: C
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019277:Entropy; D006801:Humans; D000069550:Machine Learning; D012815:Signal Processing, Computer-Assisted