Experimental Validation of LS-SVM Based Fault Identification in Analog Circuits Using Frequency Features

  • A. S. S. Vasan
  • B. Long
  • M. Pecht
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Analog circuits have been widely used in diverse fields such as avionics, telecommunications, healthcare, and more. Detection and identification of soft faults in analog circuits subjected to component variation within standard tolerance range is critical for the development of reliable electronic systems, and thus forms the primary focus of this paper. In this paper, we have experimentally demonstrated a reliable and accurate (99 %) fault diagnostic framework consisting of a sweep signal generator, spectral estimator and a least squares-support vector machine. The proposed method is completely automated and can be extended for testing other analog circuits whose performances are mainly determined by their frequency characteristics.


Support Vector Machine Power Spectral Density Fault Diagnosis Little Square Support Vector Machine Analog Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the more than 100 companies and organizations that support research activities at the Prognostics and Health Management Group within the Center for Advanced Life Cycle Engineering at the University of Maryland annually.


  1. 1.
    Birolini A (1997) Quality and reliability of technical systems. Springer, New YorkCrossRefMATHGoogle Scholar
  2. 2.
    Li F, Woo PY (2002) Fault detection for linear analog IC—the method of short-circuits admittance parameters. IEEE Trans Circuits Syst 49(1):105–108CrossRefGoogle Scholar
  3. 3.
    Alippi C, Catelani M, Fort A, Mugnaini M (2002) SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms. IEEE Trans Instrum Meas 51(5):1116–1125CrossRefGoogle Scholar
  4. 4.
    Williams A, Taylor F (2006) Electronic filter design handbook. McGraw-Hill, New YorkGoogle Scholar
  5. 5.
    Yang C, Tian S, Long B, Chen F (2011) Methods of handling the tolerance and test-points selection problem for analog-circuit fault diagnosis. IEEE Trans Instrum Meas 60(1):176–185CrossRefGoogle Scholar
  6. 6.
    Mei H, Hong W, Geng H, Shiyuan Y (2007) Soft fault diagnosis for analog circuits based on slope fault feature and BP neural network. Tsinghua Sci Technol 12(S1):26–31Google Scholar
  7. 7.
    Halgas S (2008) Multiple soft fault diagnosis of nonlinear circuits using the fault dictionary approach. Bull Pol Acad Sci 56(1):53–57MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zhou L, Shi Y, Zhao G, Zhang W, Tang H, Su L (2010) Soft-fault diagnosis of analog circuit with tolerance using mathematical programming. J Commun Comp 7(5):50–59Google Scholar
  9. 9.
    Cui J, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44:281–289CrossRefGoogle Scholar
  10. 10.
    Spina R, Upadhyaya S (1997) Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Trans Circuits Syst II 44:188–196CrossRefGoogle Scholar
  11. 11.
    Aminian M, Aminian F (2000) Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circuits Syst II 47(2):151–156CrossRefGoogle Scholar
  12. 12.
    Aminian F, Aminian M (2002) Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas 51(3):544–550CrossRefGoogle Scholar
  13. 13.
    Mohamed EA, Abdelaziz AY, Mostafa AS (2005) A neural network-based scheme for fault diagnosis of power transformers. Electr Power Syst Res 75(1):29–39CrossRefGoogle Scholar
  14. 14.
    Huang J, Hu X, Yang F (2011) Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement 44:1018–1027CrossRefGoogle Scholar
  15. 15.
    Mao X, Wang L, Li C (2008) SVM classifier for analog fault diagnosis using fractal features. In: Proceedings of the 2nd IEEE international symposium on intelligent information technology and application, pp 553–557Google Scholar
  16. 16.
    Scholkopf B, Smola A (2002) Learning with kernels-support vector machines, regularization, optimization and beyond. MIT Press, CambridgeGoogle Scholar
  17. 17.
    Suykens J, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300MathSciNetCrossRefGoogle Scholar
  18. 18.
    Hsu CW, Lin CJ (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Networks 13(2):415–425CrossRefGoogle Scholar
  19. 19.
    Long B, Tian SL, Wang HJ (2008) Least squares support vector machine based analog circuit fault diagnosis using wavelet transform as preprocessor. In: Proceedings of the international conference on communications, circuits and systems, pp 1026–1029Google Scholar
  20. 20.
    Lei Z, Ligang H, Wang Z, Wuchen W (2010) Applying wavelet support vector machine to analog circuit fault diagnosis. In: Proceedings of the 2nd workshop on education, technology and computer science, pp 75–78Google Scholar
  21. 21.
    Vapnik K (1995) The nature of statistical learning theory. Springer, New YorkCrossRefMATHGoogle Scholar
  22. 22.
    Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73MathSciNetCrossRefGoogle Scholar
  23. 23.
    Aminian M, Aminian F (2007) A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as preprocessor. IEEE Trans Instrum Meas 56(5):1546–1554CrossRefGoogle Scholar
  24. 24.
    Yuan L, He Y, Huang J, Sun Y (2010) A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans Instrum Meas 59(3):586–595CrossRefGoogle Scholar
  25. 25.
    Xiao Y, He Y (2011) A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA. Neurocomputing 74:1102–1115CrossRefGoogle Scholar
  26. 26.
    Toczek W, Zielonko R, Adamczyk A (1998) A method for fault diagnosis of nonlinear electronic circuits. Measurement 24:79–86CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Center for Advanced Life Cycle Engineering (CALCE)University of MarylandCollege ParkUSA
  2. 2.School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Center for Prognostics and System Health ManagementCity University of Hong KongKowloonHong Kong

Personalised recommendations