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Analog circuit soft fault diagnosis utilizing matrix perturbation analysis

  • Tianwen ZhangEmail author
  • Tingjun Li
Article
  • 133 Downloads

Abstract

This paper proposes a novel approach of analog circuit soft fault diagnosis utilizing matrix perturbation analysis. This method establishes an output response square matrix whose elements will change if circuits fail. Fault can be diagnosed via comparing the difference between the fault-free output matrix and the faulty. According to matrix theory, matrix spectral radius and perturbation matrix m1 norm are utilized to describe the difference. Differing from artificial intelligence algorithms, it is all completely unnecessary to train samples, and can be applied to more complex circuit diagnostics with fewer test nodes. Fault diagnosis, fault location and parameter identification can be realized by quadratic curve fitting in single fault mode. Experiments confirm the feasibility and correctness of this method.

Keywords

Analog circuit Fault diagnosis Matrix perturbation analysis Quadratic curve fitting 

Notes

References

  1. 1.
    Binu, D., & Kariyappa, B. S. (2017). A survey on fault diagnosis of analog circuits: Taxonomy and state of the art. AEÜ - International Journal of Electronics and Communications, 73, 68–83.CrossRefGoogle Scholar
  2. 2.
    Mohsen, A. K. A., & El-Yazeed, M. F. A. (2004). Selection of input stimulus for fault diagnosis of analog circuits using ARMA model. AEÜ - International Journal of Electronics and Communications, 58(3), 212–217.CrossRefGoogle Scholar
  3. 3.
    Liu, Z., Liu, T., Han, J., Bu, S., Tang, X., & Pecht, M. (2017). Signal model-based fault coding for diagnostics and prognostics of analog electronic circuits. IEEE Transactions on Industrial Electronics, 64(1), 605–614.CrossRefGoogle Scholar
  4. 4.
    El-Yazeed, M. F. A., & Mohsen, A. A. K. (2003). A preprocessor for analog circuit fault diagnosis based on Prony’s method. AEÜ - International Journal of Electronics and Communications, 57(1), 16–22.CrossRefGoogle Scholar
  5. 5.
    Yong, D., Yibing, S., & Wei, Z. (2012). Diagnosis of soft faults in analog integrated circuits based on fractional correlation. Journal of Semiconductors, 33(8), 085007.CrossRefGoogle Scholar
  6. 6.
    Okatan, A., Hajiyev, C., & Hajiyeva, U. (2009). Fault detection in sensor information fusion Kalman filter. AEÜ - International Journal of Electronics and Communications, 63(9), 762–768.CrossRefGoogle Scholar
  7. 7.
    Valinataj, M., Mohammadi, S., Plosila, J., Liljeberg, P., & Tenhunen, H. (2011). A reconfigurable and adaptive routing method for fault-tolerant mesh-based networks-on-chip. AEÜ - International Journal of Electronics and Communications, 65(7), 630–640.CrossRefGoogle Scholar
  8. 8.
    Ćirić, V., Kolokotronis, J., & Milentijević, I. (2009). Partial error tolerance for bit-plane fir filter architecture. AEÜ - International Journal of Electronics and Communications, 63(5), 398–405.CrossRefGoogle Scholar
  9. 9.
    Haidi, D., Gang, L., Junti, W., Dianheng, P., & Hui, X. (2017). Strategy for soft fault diagnosis on analog circuits with tolerance. In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (pp. 331–335). IEEE.Google Scholar
  10. 10.
    Yu, W., & He, Y. (2015). Analog circuit fault diagnosis via sensitivity computation. Journal of Electronic Testing, 31(1), 119–122.CrossRefGoogle Scholar
  11. 11.
    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 Transactions on Instrumentation and Measurement, 51(5), 1116–1125.CrossRefGoogle Scholar
  12. 12.
    Tadeusiewicz, M., Halgas, S., & Korzybski, M. (2002). An algorithm for soft-fault diagnosis of linear and nonlinear circuits. Circuits & Systems I Fundamental Theory & Applications IEEE Transactions on, 49(11), 1648–1653.MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Tang, X., & Xu, A. (2016). Multi-class classification using kernel density estimation on k-nearest neighbours. Electronics Letters, 52(8), 600–602.CrossRefGoogle Scholar
  14. 14.
    Zhang, T., & Li, T. (2019). A novel approach of analog circuit fault diagnosis utilizing RFT noise estimation. Analog Integrated Circuits and Signal Processing, 98(3), 517–526.  https://doi.org/10.1007/s10470-018-1351-x.CrossRefGoogle Scholar
  15. 15.
    Aminian, F., Aminian, M., & Collins, H. W. (2002). Analog fault diagnosis of actual circuits using neural networks. IEEE Transactions on Instrumentation and Measurement, 51(3), 544–550.CrossRefGoogle Scholar
  16. 16.
    Aminian, M., & Aminian, F. (2000). Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 47(2), 151–156.CrossRefGoogle Scholar
  17. 17.
    Ma, Q., He, Y., & Zhou, F. (2016). A new decision tree approach of support vector machine for analog circuit fault diagnosis. Analog Integrated Circuits & Signal Processing, 88(3), 455–463.CrossRefGoogle Scholar
  18. 18.
    Tan, Y., He, Y., Cui, C., & Qiu, G. (2008). A novel method for analog fault diagnosis based on neural networks and genetic algorithms. IEEE Transactions on Instrumentation and Measurement, 57(11), 2631–2639.CrossRefGoogle Scholar
  19. 19.
    Yuan, Z., He, Y., Yuan, L., & Cheng, Z. (2017). A diagnostics method for analog circuits based on improved kernel entropy component analysis. Journal of Electronic Testing, 33(6), 697–707.CrossRefGoogle Scholar
  20. 20.
    Xiao, Y., & Feng, L. (2012). A novel linear ridgelet network approach for analog fault diagnosis using wavelet-based fractal analysis and kernel PCA as preprocessors. Measurement, 45(3), 297–310.CrossRefGoogle Scholar
  21. 21.
    Golonek, T., & Machniewski, J. (2018). Analog circuit specification testing by means of Walsh–Hadamard transform and multiple regression supported by evolutionary computations. Circuits, Systems, and Signal Processing, 37(7), 2736–2771.  https://doi.org/10.1007/s00034-017-0708-1.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Han, D., Zhao, N., & Shi, P. (2017). A new fault diagnosis method based on deep belief network and support vector machine with Teager–Kaiser energy operator for bearings. Advances in Mechanical Engineering, 9(12), 168781401774311.CrossRefGoogle Scholar
  23. 23.
    He, W., He, Y., Luo, Q., & Zhang, C. (2018). Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA. Measurement Science & Technology, 29(4), 045004.  https://doi.org/10.1088/1361-6501/aaa33a.CrossRefGoogle Scholar
  24. 24.
    He, W., He, Y., Li, B., & Zhang, C. (2018). Analog circuit fault diagnosis via joint cross-wavelet singular entropy and parametric t-SNE. Entropy, 20(8), 604.CrossRefGoogle Scholar
  25. 25.
    Khanlari, M., & Ehsanian, M. (2017). An improved KFCM clustering method used for multiple fault diagnosis of analog circuits. Circuits Systems & Signal Processing, 36(9), 3491–3513.CrossRefGoogle Scholar
  26. 26.
    Zhang, A., Huang, K., Wang, R., & Zhang, Z. (2017). A novel hybrid method for analog circuit fault classification. In IEEE, data driven control and learning systems conference (pp. 365–369). IEEE.Google Scholar
  27. 27.
    Tadeusiewicz, M., & Korzybski, M. (2000). A method for fault diagnosis in linear electronic circuits. International Journal of Circuit Theory and Applications, 28(3), 245–262.CrossRefzbMATHGoogle Scholar
  28. 28.
    Liu, D., & Starzyk, J. A. (2002). A generalized fault diagnosis method in dynamic analogue circuits. International Journal of Circuit Theory and Applications, 30(5), 487–510.CrossRefzbMATHGoogle Scholar
  29. 29.
    Starzyk, J. A., Pang, J., Manetti, S., & Piccirilli, M. C. (2000). Finding ambiguity groups in low testability analog circuits. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(8), 1125–1137.MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Starzyk, J., & Liu, D. (2001). Multiple fault diagnosis of analog circuits by locating ambiguity groups of test equation. In IEEE international symposium on circuits and systems (Vol. 5, pp. 199–202). IEEE.Google Scholar
  31. 31.
    Tadeusiewicz, M., & Hałgas, S. (2006). An algorithm for multiple fault diagnosis in analogue circuits. International Journal of Circuit Theory and Applications, 34(6), 607–615.CrossRefzbMATHGoogle Scholar
  32. 32.
    Song, P., He, Y., & Cui, W. (2016). Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits. Analog Integrated Circuits & Signal Processing, 87(3), 427–436.CrossRefGoogle Scholar
  33. 33.
    Pullman, N. J. (1976). Matrix theory and its applications. New York: Marcel Dekker, Inc.zbMATHGoogle Scholar
  34. 34.
    Wilkinson, J. H. (1965). The algebraic eigenvalue problem. Oxford: Oxford University Press.zbMATHGoogle Scholar
  35. 35.
    Stewart, G. W., & Sun, J. G. (1990). Matrix perturbation theory. New York: Harcourt Brace Jovanovich.zbMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of China (UESTC)ChengduPeople’s Republic of China

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