Skip to main content
Log in

A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks

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

Abstract

To enhance the reliability of analog circuits in complex electrical systems, a novel fault diagnosis method based on conditional variational neural networks (CVNN) is presented in this paper. The CVNN model is constructed by adding a sampling layer to the multi-layer perceptron. The latent variable which has the same distribution with the original signal of the analog circuit is obtained in the sampling layer, where the noise is introduced to improve the generalization performance of the model. The output features of the sampling layer are achieved by resampling on the latent variable, and the variational inference is adopted to estimate unknown parameters of the model. To address the overfitting issue of the CVNN model, the Dropout algorithm and the scaled exponential linear unit function are applied to the hidden layers. Furthermore, the features compressed by the second hidden layer are input into the Softmax classifier for training, and then the trained fault diagnosis model is utilized to identify the fault classes of the analog circuit. The method is fully evaluated with the three typical analog circuits, and the experimental results demonstrate that the fault diagnosis method based on CVNN can achieve better diagnosis accuracy and generalization performance than other typical fault diagnosis methods for analog circuits.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

Data Availability

The authors declare that the data supporting the findings of this study are available within the article.

References

  1. A. Arabi, N. Bourouba, A. Belaout, M. Ayad, An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits. Integer. VLST J. 64, 50–59 (2019)

    Article  Google Scholar 

  2. D. Binu, K.B.S. Shida, RideNN: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Trans. Instrum. Meas. 68, 2–26 (2019)

    Article  Google Scholar 

  3. P. Diederik, W. Max, Autoencoding variational bayes. arXiv preprint arXiv:1312.6114v10 (2013)

  4. X. Gan, H. Qu, X. Meng, C. Wang, J. Zhu, Research on ELM soft fault diagnosis of analog circuit based on KSLPP feature extraction. IEEE Access 7, 92517–92527 (2019)

    Article  Google Scholar 

  5. X. Gan, W. Gao, Z. Dai, W. Liu, Research on WNN soft fault diagnosis for analog circuit based on adaptive UKF algorithm. Appl. Soft Comput. 50, 252–259 (2017)

    Article  Google Scholar 

  6. T. Gao, J. Yang, S. Jiang, C. Yang, A novel fault diagnostic method for analog circuits using frequency response features. Rev. Sci. Instrum. 90, 104708 (2019)

    Article  Google Scholar 

  7. A. Glowacz, Recognition of acoustic signals of commutator motors. Appl. Sci. Basel 8, 2630 (2018)

    Article  Google Scholar 

  8. W. He, Y. He, B. Li, C. Zhang, Analog circuit fault diagnosis via joint cross-wavelet singular entropy and parametric t-SNE. Entropy 20, 604 (2018)

    Article  Google Scholar 

  9. W. He, Y. He, Q. Luo, C. Zhang, Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA. Meas. Sci. Technol. 29, 045004 (2018)

    Article  Google Scholar 

  10. W. He, Y. He, C. Zhang, A new fault diagnosis approach for analog circuits based on spectrum image and feature weighted kernel Fisher discriminant analysis. Rev. Sci. Instrum. 89, 074702 (2018)

    Article  Google Scholar 

  11. W. He, Y. He, B. Li, C. Zhang, Feature extraction of analogue circuit fault signals via cross-wavelet transform and variational Bayesian matrix factorisation. IET Sci. Meas. Technol. 13, 318–327 (2019)

    Article  Google Scholar 

  12. Z. Hu, M. Xiao, L. Zhang, S. Liu, Y. Ge, Mahalanobis distance based approach for anomaly detection of analog filters using frequency features and Parzen window density estimation. J. Electron. Test. Theory Appl. 32, 681–693 (2016)

    Article  Google Scholar 

  13. N. Keskar, D. Mudigere, J. Nocedal. On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)

  14. Z. Li, A novel fault diagnostic method based on node-voltage vector ambiguity sets. IEEE Trans. Instrum. Meas. 63, 1957–1965 (2014)

    Article  Google Scholar 

  15. B. Long, S. Tian, H. Wang, Diagnostics of filtered analog circuits with tolerance based on LS-SVM using frequency features. J. Electron. Test. Theory Appl. 28, 291–300 (2012)

    Article  Google Scholar 

  16. H. Luo, W. Lu, Y. Wang, L. Wang, X. Zhao, A novel approach for analog fault diagnosis based on stochastic signal analysis and improved GHMM. Measurement 81, 26–35 (2016)

    Article  Google Scholar 

  17. S.M. Shokrolahi, A.T.N. Kazempour, A novel approach for fault detection of analog circuit by using improved EEMD. Analog Integr. Circ. Sig. Process 98, 527–534 (2019)

    Article  Google Scholar 

  18. N. Srivastava, G. Hinton, A. Krizhevsky, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  19. Y. Wang, Y. Ma, S. Cui, Y. Yan, A novel approach of feature extraction for analog circuit fault diagnosis based on WPD-LLE-CSA. J. Electron. Eng. Technol. 13, 2485–2492 (2018)

    Google Scholar 

  20. L. Wang, D. Zhou, H. Tian, H. Zhang, W. Zhang, Parametric fault diagnosis of analog circuits based on a semi-supervised algorithm. Symmet. Basel 11, 228 (2019)

    Article  Google Scholar 

  21. Y. Wang, Q. Jin, G. Sun, Planetary gearbox fault feature learning using conditional variational neural networks under noise environment. Knowl. Based Syst. 163, 438–449 (2019)

    Article  Google Scholar 

  22. Y. Xiao, L. Feng, A novel linear ridgelet network approach for analog fault diagnosis using wavelet-based fractal analysis and kernel PCA as preprocessors. Measurement 45, 297–310 (2012)

    Article  Google Scholar 

  23. Y. Xiao, Y. He, A linear ridgelet network approach for fault diagnosis of analog circuit. Sci. China Inf. Sci. 53, 2251–2264 (2010)

    Article  Google Scholar 

  24. J. Xiong, S. Tian, C. Yang, Fault diagnosis for analog circuits by using EEMD, relative entropy, and ELM. Comput. Intell. Neurosci. (2016). https://doi.org/10.1155/2016/7657054

    Article  Google Scholar 

  25. Z. Yuan, Y. He, L. Yuan, Z. Cheng, A diagnostics method for analog circuits based on improved kernel entropy component analysis. J. Electron. Test. Theory Appl. 33, 697–707 (2017)

    Article  Google Scholar 

  26. L. Yuan, Y. He, J. Huang, Y. Sun, A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans. Instrum. Meas. 59, 586–595 (2010)

    Article  Google Scholar 

  27. C. Zhang, Y. He, L. Yuan, S. Xiang, Analog circuit incipient fault diagnosis method using DBN based features extraction. IEEE Access 6, 23053–23064 (2018)

    Article  Google Scholar 

  28. C. Zhang, Y. He, L. Yuan, W. He, S. Xiang, Z. Li, A novel approach for diagnosis of analog circuit fault by using GMKL-SVM and PSO. J. Electron. Test. Theory Appl. 32, 531–540 (2016)

    Article  Google Scholar 

  29. G. Zhao, Y. Liu, Y. Gao, Z. Jiang, C. Hu, A new approach for analog circuit fault diagnosis based on extreme learning machine, in Prognostics and System Health Management Conference, pp. 196–200 (2018)

  30. G. Zhao, X. Liu, B. Zhang, Y. Liu, G. Niu, C. Hu, A novel approach for analog circuit fault diagnosis based on Deep Belief Network. Measurement 121, 170–178 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editors and the reviewers for their helpful comments

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jingli Yang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, T., Yang, J., Jiang, S. et al. A Novel Fault Diagnosis Method for Analog Circuits Based on Conditional Variational Neural Networks. Circuits Syst Signal Process 40, 2609–2633 (2021). https://doi.org/10.1007/s00034-020-01595-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01595-4

Keywords

Navigation