[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM

  • Published:
Journal of Electronic Testing Aims and scope Submit manuscript

Abstract

In order to estimate the remaining useful performance (RUP) of analog circuits precisely in real time, an analog circuit fault prognostics framework is proposed in the paper. Output voltages are extracted from circuit responses as features to calculate cosine distance which can reflect the health condition of analog circuits. Relevance vector machine (RVM) which has been improved by particle swarm optimization (PSO) algorithm is applied to estimate the RUP. Twelve case studies involving bandpass filter, highpass filter and nonlinear circuit have validated the predict performance of the approach. Simulation results demonstrate that the proposed approach has higher prediction precision.

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
£29.99 /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 includes VAT (United Kingdom)

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

Similar content being viewed by others

References

  1. Alam MA, Azarian MH, Osterman M, Pecht M (2011) Prognostics of failures in embedded planar capacitors using model-based and data-driven approaches. J Intell Mater Syst Struct 22(12):1293–1304

    Article  Google Scholar 

  2. Aminian M, Aminian F (2000) Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circ Syst II: Analog Digit Signal Process 47(2):151–156

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Cui J, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44(1):281–289

    Article  Google Scholar 

  5. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proc Proc Sixth Int Symp Micro Mach Hum Sci 1:39–43

    Article  Google Scholar 

  6. Goebel K, Saha B, Saxena A (2008) A comparison of three data-driven techniques for prognostics. Proc 62nd Meet Soc Mach Fail Prev Technol (MFPT), pp. 119–131

  7. Guo X, Yang J, Wu C, Liang Y (2008) A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16):3211–3215

    Article  Google Scholar 

  8. Han L, Narendran N (2011) An accelerated test method for predicting the useful life of an LED driver. IEEE Trans Power Electron 26(8):2249–2257

    Article  Google Scholar 

  9. He Y, Tan Y, Sun Y (2004) Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proc Circ Devices Syst 151(4):379–384

    Article  Google Scholar 

  10. Huang J, Hu X, Yang F (2011) Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement 44(6):1018–1027

    Article  Google Scholar 

  11. Li M, Xian W, Long B, Wang H (2013) Prognostics of analog filters based on particle filters using frequency features. J Electron Test Theory Appl 29(4):567–584

    Article  MATH  Google Scholar 

  12. Lin S, Ying K, Chen S, Lee Z (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824

    Article  Google Scholar 

  13. Nie L, Azarian MH, Keimasi M, Pecht M (2007) Prognostics of ceramic capacitor temperature-humidity-bias reliability using Mahalanobis distance analysis. Circ World 33(3):21–28

    Article  Google Scholar 

  14. Saxena A, Celaya J, Saha B, Saha S, Goebel K (2010) Metrics for offline evaluation of prognostic performance. Int J Prognostics Health Manag 1(1):1–20

    Google Scholar 

  15. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In Proc. Proceedings of the International Congress on Evolutionary Computation. IEEE Service Center, Piscataway, pp 69–73

    Google Scholar 

  16. Shi Y, Deng Y, Zhang W (2013) Diagnosis of incipient faults in weak nonlinear analog circuits. Circ Syst Signal Proc 32(5):2151–2170

    Article  MathSciNet  Google Scholar 

  17. Tan Y, Sun Y (2013) Analog fault diagnosis using S-transform preprocessor and a QNN classifier. Measurement 46(7):2174–2183

    Article  Google Scholar 

  18. Tan Y, He Y, Sun Y, Yang H, Liu M (2009) Data-fused method of fault diagnosis for analog circuits. Analog Integr Circ Signal Proc 61(1):87–92

    Article  Google Scholar 

  19. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    MATH  MathSciNet  Google Scholar 

  20. Vasan ASS, Long B, Pecht M (2013) Diagnostics and prognostics method for analog electronic circuits. IEEE Trans Ind Electron 60(11):5277–5291

    Article  Google Scholar 

  21. Wang D, Miao Q, Pecht M (2013) Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. J Power Sources 239:253–264

    Article  Google Scholar 

  22. Wang HS, Wang YN, Wang YC (2013) Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Systems with Applications 40(2):418–428

    Article  Google Scholar 

  23. Widodo A, Shim MC, Caesarendra W, Yang BS (2011) Intelligent prognostics for battery health monitoring based on sample entropy. Expert Syst Appl 38(9):11763–11769

    Article  Google Scholar 

  24. Xiao Y, Feng L (2012) A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization. Appl Soft Comput 12(2):904–920

    Article  MathSciNet  Google Scholar 

  25. Xiao Y, He Y (2011) A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA. Neurocomputing 74(7):1102–1115

    Article  Google Scholar 

  26. Xu L, Huang J, Wang H, Long B (2010) A novel method for the diagnosis of the incipient faults in analog circuits based on LDA and HMM. Circ Syst Signal Proc 29(4):577–600

    Article  MATH  MathSciNet  Google Scholar 

  27. Yang C, Tian S, Long B, Chen F (2011) Methods of handling the tolerance and test-point selection problem for analog-circuit fault diagnosis. IEEE Trans Instrum Meas 60(1):176–185

    Article  Google Scholar 

  28. Yin X, Sun Y, Wang C (2013) Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM. Abstract and Applied Analysis Article ID 737146. doi:10.1155/2013/737146

  29. Yuan L, He Y, Huang J, Sun Y (2010) A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans Instrum Meas 59(3):586–595

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Funds of China for Distinguished Young Scholar under Grant No. 50925727, the National Defense Advanced Research Project Grant No. C1120110004, 9140A27020211DZ5102, the Key Grant Project of Chinese Ministry of Education under Grant No.313018, Anhui Provincial Science and Technology Foundation of China under Grant No. 1301022036, the Fundamental Research Funds for the Central Universities No.2012HGCX0003 and National Natural Science Foundation of China No.61102035.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Chaolong Zhang, Yigang He, Lifen Yuan & Fangming Deng

  2. School of Physics and Electronic Engineering, Anqing Normal University, Anqing, 246011, China

    Chaolong Zhang

Authors
  1. Chaolong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yigang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lifen Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fangming Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaolong Zhang.

Additional information

Responsible Editor: A. Ivanov

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., He, Y., Yuan, L. et al. A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM. J Electron Test 30, 343–356 (2014). https://doi.org/10.1007/s10836-014-5454-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10836-014-5454-8

Keywords

Search

Navigation