Journal of Electronic Testing

, Volume 17, Issue 6, pp 471–481 | Cite as

Fault Diagnosis of Nonlinear Analog Circuits Using Neural Networks with Wavelet and Fourier Transforms as Preprocessors

  • Farzan Aminian
  • Mehran Aminian


A neural-network based analog fault diagnostic system is developed for nonlinear circuits. This system uses wavelet and Fourier transforms, normalization and principal component analysis as preprocessors to extract an optimal number of features from the circuit node voltages. These features are then used to train a neural network to diagnose soft and hard faulty components in nonlinear circuits. Our neural network architecture has as many outputs as there are fault classes where these outputs estimate the probabilities that input features belong to different fault classes. Application of this system to two sample circuits using SPICE simulations shows its capability to correctly classify soft and hard faulty components in 95% of the test data. The accuracy of our proposed system on test data to diagnose a circuit as faulty or fault-free, without identifying the fault classes, is 99%. Because of poor diagnostic accuracy of backpropagation neural networks reported in the literature (Yu et al., Electron. Lett., Vol. 30, 1994), it has been suggested that such an architecture is not suitable for analog fault diagnosis (Yang et al., IEEE Trans. on CAD, Vol. 19, 2000). The results of the work presented here clearly do not support this claim and indicate this architecture can provide a robust fault diagnostic system.

fault diagnosis neural networks wavelet transform Fourier transform nonlinear circuits electronic circuits analog circuits 


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  1. 1.
    M. Aminian and F. Aminian, “Neural Network Based Analog Circuit Fault Diagnosis Using Wavelet Transform as Preprocessor,” IEEE Transactions on Circuits and Systems II, Vol. 47,No. 2, pp. 151-156, 2000.Google Scholar
  2. 2.
    J.W. Bandler and A.E. Salama, “Fault Diagnosis of Analog Circuits,” in Proc. IEEE, Vol. 73, pp. 1279-1325, 1985.Google Scholar
  3. 3.
    C.M. Bishop, Neural Networks for Pattern Recognition, New York, NY: Oxford University Press, 1995.Google Scholar
  4. 4.
    M. Catelani and M. Gori, “On the Application of Neural Networks to Fault Diagnosis of Electronic Analog Circuits,” Measurement, Vol. 17, pp. 73-80, 1996.Google Scholar
  5. 5.
    M.T. Hagan, H.B. Demuth, and Mark Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.Google Scholar
  6. 6.
    S.L. Hurst, VLSI Testing: Digital and Mixed Analogue/Digital Techniques, Institution of Electrical Engineers, Stevenage, England, 1998.Google Scholar
  7. 7.
    F. Li and P. Woo, “Fault Detection Method for the Subcircuits of a Cascade Linear Circuits,” IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 47,No. 8, 2000.Google Scholar
  8. 8.
    R.-W. Liu, Testing and Diagnosis of Analog Circuits and Systems, New York, NY: Van Nostrand Reinhold, 1991.Google Scholar
  9. 9.
    K. Madani, A. Bengharbi, and V. Amarger, “Neural Fault Diagnosis Techniques for Nonlinear Analogue Circuits,” SPIE, Vol. 3077,No. 3, pp. 491-502, 1997.Google Scholar
  10. 10.
    J. Meador, A. Wu, C.T. Tseng, and T.S. Lin, “Fast Diagnosis of Integrated Circuit Faults Using Feed Forward Neural Networks,” in Proceedings of IJCNN, 1991.Google Scholar
  11. 11.
    R.G. Miller, “The Jackknife-A Review,” Biometrika, Vol. 61, 1974.Google Scholar
  12. 12.
    E. Parzen, “On Estimation of a Probability Density Function and Mode,” Ann. Math. Statistics, Vol. 3, 1962.Google Scholar
  13. 13.
    K. Saab, N.B. Hamida, and B. Kaminska, “Closing the Gap Between Analog and Digital Testing,” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol. 20,No. 2, 2001.Google Scholar
  14. 14.
    Selected Papers on Analog Fault Diagnosis, New York, NY: IEEE Press, 1987.Google Scholar
  15. 15.
    R. Spina and S. Upadhyaya, “Linear Circuit Fault Diagnosis Using Neuromorphic Analyzer,” IEEE Transactions on Circuits and Systems II, Vol. 44,No. 3, 1997.Google Scholar
  16. 16.
    G. Strang and T. Nguyen. Wavelet and Filter Banks, Wellesley, MA: Wellesley-Cambridge Press, 1996.Google Scholar
  17. 17.
    Z.R. Yang, M. Zwolinski, C.D. Chalk, and A.C. Williams, “Applying a Robust Heteroscedastic Probabilistic Neural Network to Analog Fault Detection and Classification,” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, Vol. 19,No. 1, 2000.Google Scholar
  18. 18.
    S. Yu, B.W. Jervis, K.R. Eckersall, I.M. Bell, A.G. Hall, and G.E. Taylor, “Neural Network Approach to Fault Diagnosis in Cmos Opamps with Gate Oxide Short Faults,” Electron. Lett., Vol. 30, 1994.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Farzan Aminian
    • 1
  • Mehran Aminian
    • 2
  1. 1.Trinity UniversitySan AntonioUSA
  2. 2.St. Mary's University, One Camino Santa MariaSan AntonioUSA

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