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Diagnostics of Analog Circuits Based on LS-SVM Using Time-Domain Features

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Abstract

Most researchers use wavelet transforms to extract features from a time-domain transient response from analog circuits to train classifiers such as neural networks (NNs) and support vector machines (SVMs) for analog circuit diagnostics. In this paper, we have proposed some new feature selection methods from a time-domain transient response, and compared the diagnostic results based on a least squares SVM (LS-SVM) using different time-domain feature vectors. First, we have improved two traditional feature selection methods: (a) using the mean and standard deviation in wavelet transform features, and (b) using the mean, standard deviation, skewness, kurtosis, and entropy in statistical property features. Then, a conventional time-domain feature vector based on the impulse response properties of a control system has been proposed. The simulation experiments for a leapfrog filter and a nonlinear rectifier show that: (1) the two improved methods have better accuracy than the traditional methods; (2) the proposed conventional time-domain feature vector is effective in the diagnostics of analog circuits—over 99 % for both of the two example circuits; (3) the proposed diagnostic method can diagnose soft faults, hard faults, and multi-faults, regardless of component tolerances and nonlinearity effects.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61071029, 60934002, 61201009, and 61271035, in part by the Doctoral Fund of the Ministry of Education of China under Grant 20100185110004, in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2012J088, and in part by UESTC under Grant Y02018023601059. The authors would like to thank all anonymous reviewers for their valuable comments on this paper.

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  1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Bing Long, Min Li, Houjun Wang & Shulin Tian

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  1. Bing Long
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  2. Min Li
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  3. Houjun Wang
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  4. Shulin Tian
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Correspondence to Bing Long.

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Long, B., Li, M., Wang, H. et al. Diagnostics of Analog Circuits Based on LS-SVM Using Time-Domain Features. Circuits Syst Signal Process 32, 2683–2706 (2013). https://doi.org/10.1007/s00034-013-9614-3

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  • DOI: https://doi.org/10.1007/s00034-013-9614-3

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