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.
Highlights
Numerous researches have indicated that analog circuit fault diagnosis is a significant fundamental for design validation and performance evaluation in the integrated circuit manufacturing fields [1,2,3]
Many feature extraction methods have been proposed such as correlation function technique [4], information entropy approach [5], the fast Fourier transform technique [6], and the wavelet transform technique [7]
Aminian proposed a diagnostic method of analog circuits using wavelet decomposition coefficients, principal component analysis (PCA), and data normalization to construct fault feature vectors and trained and tested neural network classifiers [3]
Summary
Numerous researches have indicated that analog circuit fault diagnosis is a significant fundamental for design validation and performance evaluation in the integrated circuit manufacturing fields [1,2,3]. Aminian proposed a diagnostic method of analog circuits using wavelet decomposition coefficients, principal component analysis (PCA), and data normalization to construct fault feature vectors and trained and tested neural network classifiers [3]. In this paper, we decomposed impulse responses of a CUT into IMFs using EEMD method and adopting kurtosis and relative entropy techniques to obtain feature vectors These features vectors can be used for diagnosis of faulty components among various variation possibilities. Utilizing the combination of EEMD, relative entropy, and ELM algorithms for feature extraction and classification we can complete analog circuit fault diagnosis. It demonstrates reliable and accurate fault diagnosis with reduced test time.
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