Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Aiming at the problem to diagnose incipient faults in weak nonlinear analog circuits, an approach is presented in this paper. The approach calculates the fractional Volterra correlation functions beforehand. The next step is to use the fractional Volterra correlation functions and different angle parameters of the fractional wavelet packet transform (FRWPT) to extract the fault signatures. Meanwhile, the computational complexity is analyzed. Then the variables of the fault signatures are constructed, which are used to form the observation sequences of the hidden Markov model (HMM). HMM is used to accomplish the fault diagnosis. The simulations show that the presented method can significantly improve the incipient fault diagnosis capability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. O. Akay, G.F. Boudreaux-Bartels, Fractional convolution and correlation via operator methods and an application to detection of linear FM signals. IEEE Trans. Signal Process. 49(5), 979–993 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. M. Aminian, F. Aminian, Neural-network based analog circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans. Circuits Syst. II 47(2), 151–156 (2000)

    Article  Google Scholar 

  4. J.W. Bandler, A.E. Salama, Fault diagnosis of analog circuits. Proc. IEEE 73(8), 1279–1325 (1985)

    Article  Google Scholar 

  5. M. Catelani, A. Fort, Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy method and radial basis function networks. IEEE Trans. Instrum. Meas. 51(2), 196–202 (2002)

    Article  Google Scholar 

  6. Y. Deng, Y. Shi, W. Zhang, An approach to locate parametric faults in nonlinear analog circuits. IEEE Trans. Instrum. Meas. 61(2), 358–367 (2012)

    Article  Google Scholar 

  7. C. Evans, D. Rees, L. Jones et al., Periodic signals for measuring nonlinear Volterra kernels. IEEE Trans. Instrum. Meas. 45(2), 362–371 (1996)

    Article  Google Scholar 

  8. X. Fan, Y. Zhu, Study on fault phase selection based on FFT and phase-separation current phase difference of high-voltage transmission lines, in ICMA (2010), pp. 762–767

    Google Scholar 

  9. G.O. Glentis, P. Koukoulas, N. Kalouptsidis, Efficient algorithms for Volterra system identification. IEEE Trans. Signal Process. 47(11), 3042–3057 (1999)

    Article  Google Scholar 

  10. G.H. Golub, C.F.V. Loan, Matrix Computations, 3rd edn. (Johns Hopkins University Press, Baltimore, 1996)

    Google Scholar 

  11. Z. Huang, C. Liu, R. Liu, Node fault diagnosis and a design of testability. IEEE Trans. Circuits Syst. 30(5), 257–265 (1983)

    Article  Google Scholar 

  12. B. Jiang, P. Shi, Z.H. Mao, Sliding mode observer-based fault estimation for nonlinear networked control systems. Circuits Syst. Signal Process. 30, 1–16 (2011)

    Article  MathSciNet  Google Scholar 

  13. B.H. Juang, L.R. Rabiner, Hidden Markov models for speech recognition. Technometrics 33(3), 251–272 (1991)

    Article  MathSciNet  Google Scholar 

  14. S.N. Kher, G.M. Bubel, Predicting system-failure risk from unanticipated fiber-breaks in manufacturing. IEEE Trans. Reliab. 47(2), 126–130 (1998)

    Article  Google Scholar 

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

    Google Scholar 

  16. F. Li, P.Y. Woo, Fault detection for linear analog IC—the method of short-circuits admittance parameters. IEEE Trans. Circuits Syst. I 49(1), 105–108 (2002)

    Article  Google Scholar 

  17. L. Milor, V. Visvanathan, Detection of catastrophic faults in analog integrated circuits. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 8(2), 114–130 (1989)

    Article  Google Scholar 

  18. A.A.K. Mohsen, M.F.A. El-Yazeed, Selection of input stimulus for fault diagnosis of analog circuits using ARMA model. AEÜ, Int. J. Electron. Commun. 58(3), 212–217 (2004)

    Article  Google Scholar 

  19. W. Nelson, Weibull prediction of a future number of failures. Qual. Reliab. Eng. Int. 16, 23–26 (2000)

    Article  Google Scholar 

  20. O.T. Ogunyemi, P.I. Nelson, Prediction of gamma failure times. IEEE Trans. Reliab. 46(3), 400–405 (1997)

    Article  Google Scholar 

  21. L.R. Rabiner, B.H. Juang, An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–15 (1986)

    Article  Google Scholar 

  22. J. Roh, J.A. Abraham, Subband filtering for time and frequency analysis of mixed-signal circuit testing. IEEE Trans. Instrum. Meas. 53(2), 602–611 (2004)

    Article  Google Scholar 

  23. W.J. Rugh, Nonlinear System Theory—The Volterra/Wiener Approach (Johns Hopkins University Press, Baltimore, 1981)

    Google Scholar 

  24. M. Slamani, B. Kaminska, Analog circuit fault diagnosis based on sensitivity computation and functional testing. IEEE Des. Test Comput. 5, 30–39 (1992)

    Article  Google Scholar 

  25. J.A. Starzyk, D. Liu, Z.H. Liu, D.E. Nelson, J.O. Rutkowski, Entropy-based optimum test nodes selection for the analog fault dictionary techniques. IEEE Trans. Instrum. Meas. 53(3), 754–761 (2004)

    Article  Google Scholar 

  26. S.M. Virk, A. Muhammad, A.M. Martinez-Enriquez, Fault prediction using artificial neural network and fuzzy logic, in The 7th Mexican International Conference on Artificial Intelligence (2008), pp. 149–154

    Google Scholar 

  27. P. Wang, S.Y. Yang, A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math. IEEE Trans. Circuits Syst. I 52(10), 2118–2127 (2005)

    Article  MathSciNet  Google Scholar 

  28. L.J. Xu, J.G. Huang, H.J. Wang, B. Long, A novel method for the diagnosis of the incipient faults in analog circuits based on LDA and HMM. Circuits Syst. Signal Process. 29, 577–600 (2010)

    Article  MathSciNet  Google Scholar 

  29. J. Yang, Y.S. Xu, C.S. Chen, Human action learning via hidden Markov model. IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 27(1), 34–44 (1997)

    Article  Google Scholar 

  30. S.K. Yang, A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE Trans. Reliab. 52(3), 373–383 (2003)

    Article  Google Scholar 

  31. S.K. Yang, T.S. Liu, A Petri net approach to early failure detection and isolation for preventive maintenance. Qual. Reliab. Eng. Int. 14, 319–330 (1998)

    Article  Google Scholar 

  32. L.F. Zhou, Y.B. Shi, J.Y. Tang, Y.J. Li, Soft fault diagnosis in analog circuit based on fuzzy and direction vector, in Metrol. Meas. Syst., vol. XVI(1) (2009), pp. 61–75

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the reviewers and the editors for their constructive comments and suggestions.

This work is supported by Program for New Century Excellent Talents in University (NCET-05-0804) and partly supported by Chinese National Programs for High Technology Research and Development (2006AA06Z222).

Author information

Authors and Affiliations

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

    Yibing Shi, Yong Deng & Wei Zhang

  2. School of Electronics and Information Engineering, Southwest Petroleum University, Chengdu, 610500, China

    Yong Deng

Authors
  1. Yibing Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Deng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, Y., Deng, Y. & Zhang, W. Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits. Circuits Syst Signal Process 32, 2151–2170 (2013). https://doi.org/10.1007/s00034-013-9589-0

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-013-9589-0

Keywords

Search

Navigation