Skip to main content

Advertisement

Log in

The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM

Journal of Electronic Testing Aims and scope Submit manuscript

Abstract

Developing fault detection is very important for improving the equipment reliability and saving energy consumption. As compared with neural networks, extreme learning machine (ELM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. This paper presents a novel approach for fault detection and diagnosis based on Firefly-Chaos Algorithm and Extreme Learning Machine. The experiment result indicates this proposed method is effective for Analog Circuit fault detection and diagnosis and the model generalization ability is favorable.

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.

Institutional subscriptions

Fig 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Aminian M, Aminian F (2007) A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor. IEEE Trans Instrum Meas 56(5):1546–1554

  2. Arul R, Velusami S, Ravi G (2013) Chaotic Firefly algorithm to solve economic load dispatch problems. In: Proc. IEEE international conference on green computing,communications and conservation of energy; pp. 458–64.

  3. Baykasoğlu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164

    Article  Google Scholar 

  4. Fister I Jr et al. (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165

    MathSciNet  MATH  Google Scholar 

  5. Gandomi AH, Yang XS, Talathari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MathSciNet  MATH  Google Scholar 

  6. Guio-Ching-Liao (2006) Application of a Fuzzy Neural network combined with a chaotic Genetic algorithm and simulated annealing to short term load forecasting. IEEE Trans Evol Comput 10(3):330–340

    Article  Google Scholar 

  7. Haiying Y, Guangju C, Yongle X Feature evaluation and extraction based on neural network in analog circuit fault diagnosis. J Syst Eng Electron 18(2):434–436

  8. He Y, Zhu W, Yantao Z, Meirong L (2010) An analog circuit diagnosis method based on particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society 06:163–171

    Google Scholar 

  9. Liu H, Chen G, Song G, Liu H, Han T (2009) Analog circuit fault diagnosis using bagging ensemble method with cross-validation. Proc. International Conf. on Mechatronics and Automation, ICMA 2009, Changchun, pp 4430–4434

  10. Marin CV, Constantinescu F, Nitescu, M (2011) A dictionary approach to fault diagnosis of analog circuits. In: Proc. AFRICON, Livingstone, pp 1–5, 13–15 Sept 2011

  11. Metlicka M, Davendra D (2015) Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems. Swarm and Evolutionary Computation 25:15–28

    Article  Google Scholar 

  12. Ott E (2002) Chaos in dynamical systems. Cambridge University Press, UK, Cambridge

  13. Chen S, Zhao S, Wang C (2013) A new analog circuit fault diagnosis approach based on GA-SVM. In: Proc. IEEE TENCON Region 10 conference, pp 1–4, 22–25 Oct 2013

  14. Sindia S, Agrawal VD, Singh V (2012) Defect Level and Fault Coverage in Coefficient Based Analog Circuit Testing. J Electron Test 28(4):541–549.

  15. Xiao-Jian L, Yang G-H (2014) Fault Detection in Finite Frequency Domain for Takagi-Sugeno Fuzzy Systems With Sensor Faults. IEEE Transactions on Cybernetics 44(8):1446–1458

  16. Qin X, Han B, Cui L (2011) A kind integrated adaptive fuzzy neural network tolerance analog circuit fault diagnosis method. In: Proc. 2nd International Conf. on Computing, Control and Industrial Engineering (CCIE), pp 180–183, Aug 2011

  17. Yu S et al. (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214–220

    MathSciNet  Google Scholar 

  18. Yunyan H, Minfang P, Chenglai T, Hu T (2012) Analog Circuit Fault Diagnosis Using Multi-wavelet Transform and SVM. In: Proc.International Conf. on Digital Manufacturing and Automation, pp 214–217

  19. Zhang A, Chen C (2014) Fault diagnosis based semi-supervised global LSSVM for analog circuit. In: Proc. International Conf. on Mechatronics and Control (ICMC), pp 744–748

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to WenXin Yu.

Additional information

Responsible Editor: S. Sindia

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, W., Sui, Y. & Wang, J. The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM. J Electron Test 32, 459–465 (2016). https://doi.org/10.1007/s10836-016-5597-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10836-016-5597-x

Keywords

Navigation