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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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References

  1. M. Aminian, F. Aminian, Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans. Circuits Syst. II, Express Briefs 47, 151–156 (2000)

    Article  Google Scholar 

  2. F. Aminian, M. Aminian, Fault diagnosis of nonlinear circuits using neural networks with wavelet and Fourier transforms as preprocessors. J. Electron. Test., Theory Appl. 17, 471–481 (2001)

    Article  Google Scholar 

  3. F. Aminian, M. Aminian, Analog fault diagnosis of actual circuits using neural networks. IEEE Trans. Instrum. Meas. 51, 544–550 (2002)

    Article  Google Scholar 

  4. M. Aminian, F. Aminian, A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor. IEEE Trans. Instrum. Meas. 56, 1546–1554 (2007)

    Article  Google Scholar 

  5. C. Chen, D. Brown, C. Sconyers, G. Vachtsevanos, B. Zhang, M.E. Orchard, A .NET framework for an integrated fault diagnosis and failure prognosis architecture, in IEEE Autotestcon, Orlando, FL (2010), pp. 1–6

    Google Scholar 

  6. C. Chen, B. Zhang, G. Vachtsevanos, M. Orchard, Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Trans. Ind. Electron. 58, 4353–4364 (2011)

    Article  Google Scholar 

  7. J. Cui, Y.R. Wang, A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44, 281–289 (2011)

    Article  Google Scholar 

  8. J. Dai, D. Das, M. Pecht, Prognostic-based risk mitigation for telecom equipment under free air cooling conditions. Appl. Energy 99, 423–429 (2012)

    Article  Google Scholar 

  9. C.W. Hsu, C.J. Lin, A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13, 415–425 (2002)

    Article  Google Scholar 

  10. J. Huang, X. Hu, F. Yang, Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement 44, 1018–1027 (2011)

    Article  Google Scholar 

  11. K. John, OrCAD Pspice and Circuit Analysis, 4th edn. (Prentice-Hall, Upper Saddle River, 2000)

    Google Scholar 

  12. R. Kondagunturi, E. Bradley, K. Maggard, C. Stroud, Benchmark circuits for analog and mixed-signal testing, in Proc. of IEEE Southeastcon’99, Lexington, KY (1999), pp. 217–220

    Google Scholar 

  13. G.Y. Li, K.M. Xie, L.J. Yang, Computer Simulation and Computer Aided Design Technology for Control System Based on Matlab (China Publishing House of Electronics Industry (PHEI), Beijing, 2008)

    Google Scholar 

  14. Y.H. Liu, Y.Y. Yang, H. Huang, Fault diagnosis of analog circuit based on support vector machines, in Proc. of ICCTA2009, Beijing (2009), pp. 40–43

    Google Scholar 

  15. B. Long, S.L. Tian, H.J. Wang, Least squares support vector machine based analog-circuit fault diagnosis using wavelet transform as preprocessor, in ICCCAS08, Fujian (2008), pp. 1026–1029

    Google Scholar 

  16. B. Long, S.L. Tian, Q. Miao, M. Pecht, Research on features for diagnostics of filtered analog circuits based on LS-SVM, in IEEE Autotestcon, Baltimore, MD (2011), pp. 360–366

    Google Scholar 

  17. B. Long, S.L. Tian, H.J. Wang, Diagnostics of filtered analog circuits with tolerance based on LS-SVM using frequency features. J. Electron. Test., Theory Appl. 28, 291–300 (2012)

    Article  Google Scholar 

  18. C. Nikias, A. Petropulu, High-Order Spectra Analysis: A Nonlinear Signal Processing Framework (Prentice-Hall, Englewood Cliffs, 1993)

    Google Scholar 

  19. L. Rapisarda, R. Decarlo, Analog multifrequency fault diagnosis. IEEE Trans. Circuits Syst. 30, 223–234 (1983)

    Article  MATH  Google Scholar 

  20. L. Rapisarda, R. Decarlo, Fault diagnosis under a limited-fault assumption and limited test-point availability. Circuits Syst. Signal Process. 7, 481–510 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  21. B. Scholkopf, A. Smola, Learning with Kernels—Support Vector Machines. Regularization, Optimization and Beyond (MIT Press, Cambridge, 2002)

    Google Scholar 

  22. M.S.J. Seyyed, K. Mohammadi, Evolutionary derivation of optimal test sets for neural network based analog and mixed signal circuits fault diagnosis approach. Microelectron. Reliab. 49, 199–208 (2009)

    Article  Google Scholar 

  23. G.W. Snedecor, G. Cochran William, Statistical Methods (Iowa State University Press, Ames, 1989)

    MATH  Google Scholar 

  24. R. Spina, S. Upadhyaya, Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Trans. Circuits Syst. II, Express Briefs 44, 188–196 (1997)

    Article  Google Scholar 

  25. J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle, Least Squares Support Vector Machines (World Scientific, Singapore, 2002)

    Book  MATH  Google Scholar 

  26. A. Tafazzoli, N.M. Steiger, J.R. Wilson, N-Skart: a nonsequential skewness- and auto regression-adjusted batch-means procedure for simulation analysis. IEEE Trans. Autom. Control 56, 254–264 (2011)

    Article  MathSciNet  Google Scholar 

  27. R. Voorakaranam, S.S. Akbay, Signature testing of analog and RF circuits: algorithms and methodology. IEEE Trans. Circuits Syst. I, Regul. Pap. 54, 1018–1031 (2007)

    Article  Google Scholar 

  28. C.L. Yang, S.L. Tian, B. Long, Methods of handling the tolerance and test-point selection problem for analog-circuit fault diagnosis. IEEE Trans. Instrum. Meas. 60, 176–185 (2011)

    Article  Google Scholar 

  29. L.F. Yuan, Y.G. He, J.Y. Huang, Y.C. Sun, A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans. Instrum. Meas. 59, 586–595 (2010)

    Article  Google Scholar 

  30. Y. Zhang, X.Y. Wei, H.F. Jiang, One-class classifier based on SBT for analog circuit fault diagnosis. Measurement 41, 371–380 (2008)

    Article  Google Scholar 

  31. L. Zuo, L.G. Hou, W. Zhang, W.C. Wu, Applying wavelet support vector machine to analog circuit fault diagnosis, in 2010 Second International Workshop on Education Technology and Computer Science, Wuhan (2010), pp. 75–78

    Google Scholar 

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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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  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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