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Journal of Electronic Testing

, Volume 30, Issue 2, pp 243–249 | Cite as

Soft Fault Diagnosis of Analog Circuits via Frequency Response Function Measurements

  • Yongle Xie
  • Xifeng Li
  • Sanshan Xie
  • Xuan Xie
  • Qizhong Zhou
Article

Abstract

This paper provides a novel method for single and multiple soft fault diagnosis of analog circuits. The method is able to locate the faulty elements and evaluate their parameters. It employs the information contained in the frequency response function (FRF) measurements and focuses on finding models of the circuit under test (CUT) as exact as possible. Consequently, the method is capable of getting different sets of the parameters which are consistent with the diagnostic test, rather than only one specific set. To fulfil this purpose, the local plolynomial approach is applied and the associated normalized FRF is developed.The proposed method is especially suitable at the pre-production stage, where corrections of the technological design are important and the diagnostic time is not crucial. Two experimental examples are presented to clarify the proposed method and prove its efficiency.

Keywords

Analog circuit Component tolerance Soft fault Frequency response function Fisher information 

Notes

Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (Grant no. 61371049), the National Basic Research Program of China (973 Program)(Grant no. 2014CB744206), the Specialized Research Fund for the Doctoral Program of High Education of China (Grant no. 20120185110013) and Sichuan Province Applied Basic Research Project (Grant no. 2013JY0058) for their support of this research.

References

  1. 1.
    Aminian F, Aminian M, Collins H (2002) Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas 51(3):544–550CrossRefGoogle Scholar
  2. 2.
    Bandler J, Salama A (1985) Fault diagnosis of analog circuits. Proc IEEE 73:1279–1325CrossRefGoogle Scholar
  3. 3.
    Bercher JF, Vignat C (2009) On minimum Fisher information distributions with restricted support and fixed variance. Inf Sci 179:3832–3842CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Butcher S, Sheppard J (2009) Distributional smoothing in bayesian fault diagnosis. IEEE Trans Instrum Meas 58(2):342–349CrossRefGoogle Scholar
  5. 5.
    Flego SP, Plastino A, Plastino AR (2011) Fisher information, the Hellman-Feynman theorem, and the Jaynes reciprocity relations. Annals Phys 326:2533–2543CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Frieden BR (1998) Physics from Fisher information measure. Cambridge Univeresity Press, CambridgeCrossRefGoogle Scholar
  7. 7.
    Iuculano G, Nielsen L, Zanobini A, Pellegrini G (2007) The principle of maximum entropy applied in the evaluation of the measurement uncertainty. IEEE Trans Instrum Meas 56(3):717–722CrossRefGoogle Scholar
  8. 8.
    Starzyk JA, Liu D, Liu Z-H, Nelson DE, Rutkowski JO (2004) Entropy-based optimum test points delection for analog fault dictionary techniques. IEEE Trans Instrum Meas 53(3):754–761Google Scholar
  9. 9.
    Kavithamani A, Manikandan V, Devarajan N (2012) Fault detection of analog circuits using network parameters. Int J Electron Test Theory Appl 28:257–261. SpringerCrossRefGoogle Scholar
  10. 10.
    Kaminska B, Arabi K, Bell I, Goteti P, Huertas JL, Kin B, Rueda A, Soma M (1997) Analog and mixed-signal benchmark circuits - first release. Proc IEEE Int Test Conf:183–190Google Scholar
  11. 11.
    Lataire J, Louarroudi E, Pintelon R (2012) Detecting a time-varying behavior in frequency response function measureemnts. IEEE Trans Instrum Meas 61(8):2132–2143CrossRefGoogle Scholar
  12. 12.
    Li XF, Xie YL (2013) Analog circuits fault detection using cross-entropy approach. Int J Electron Test Theory Appl 29:115–120. SpringerCrossRefMathSciNetGoogle Scholar
  13. 13.
    Papakostas D, Hatzopoulos A (1993) Correlation-based comparison of analog signatures for identification and fault diagnosis. IEEE Trans Instrum Meas 42(4):860–863CrossRefGoogle Scholar
  14. 14.
    Papoulis A (1965) Probability, random variables and stochastic processes, ch.10. McGraw-Hill, New YorkGoogle Scholar
  15. 15.
    Pintelon R, Schoukens J, Vandersteen G, Barbe K (2010) Estimation of nonparametric noise and FRF models for multivariable systems-Part I: Theory. Mech Sys Signal Process 24(3):573–595CrossRefGoogle Scholar
  16. 16.
    Stefan V, Omar E, Colin T, Frank O (2011) Challenges for semiconductor test engineering: a review paper. JETTA 28(3):365–374. SpringerGoogle Scholar
  17. 17.
    Stoica P, Selen R (2004) A review of information criterion rules. Signal Process Mag IEEE 21(4):36–47CrossRefGoogle Scholar
  18. 18.
    Sindia S, Agrawal VD, Singh V (2012) Parametric fault testing of non-linear analog circuits based on polynomial and V-transform coefficients. Int J Electron Test Theory Appl 28:757–771. SpringerGoogle Scholar
  19. 19.
    Yang T, Yi H, Chun C, Guan Q (2008) A novel method for analog fault diagnosis based on neural netwoks and genetic algorithms. IEEE Trans Instrum Meas 57(11):2631–2639CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yongle Xie
    • 1
  • Xifeng Li
    • 1
  • Sanshan Xie
    • 2
  • Xuan Xie
    • 1
  • Qizhong Zhou
    • 1
  1. 1.School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Chengdu Technological UniversityChengduChina

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