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A Novel Approach of Feature Extraction for Analog Circuit Fault Diagnosis Based on WPD-LLE-CSA

  • Wang, Yuehai (Dept. of Electronic Information Engineering, North China University of Technology) ;
  • Ma, Yuying (Dept. of Electronic Information Engineering, North China University of Technology) ;
  • Cui, Shiming (Dept. of Computer Science, North China University of Technology) ;
  • Yan, Yongzheng (Dept. of Computer Science, North China University of Technology)
  • Received : 2018.02.22
  • Accepted : 2018.06.12
  • Published : 2018.11.01

Abstract

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.

Keywords

References

  1. W. Peng, Y. Shiyuan, "A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math," IEEE Trans. Circuits Syst. I Reg. Papers, vol. 52, no. 10, pp. 2118-2127, 2005. https://doi.org/10.1109/TCSI.2005.853266
  2. Tan, Y., et al. "A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms[J]," IEEE Transactions on Instrumentaion and Measurement, vol. 57, no. 11, pp. 2631-2639, 2008. https://doi.org/10.1109/TIM.2008.925009
  3. Aminian, M. and F. Aminian, "A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks with Wavelet Transform as a Preprocessor [J]," IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 5, pp. 1546-1554, 2007. https://doi.org/10.1109/TIM.2007.904549
  4. Song Liwei, "Fault Diagnosis of Analog Circuits Based on Wavelet Analysis and Neural Network. [D]," Hunan University, 2012.
  5. Wang, Yuehai, Yongzheng Yan, and Qinyong Wang. "Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit," Mathematical Problems in Engineering, 2016.
  6. Liao Jian, Zhou Shaolei, Shi Xianjun, Wang Zhen. "Reducing Dimension of Analog Circuit Fault Feature [J]," Journal of Vibration, Measurement & Diagnosis, pp. 02:302-308+400, 2015.
  7. Luo, Hui, Wang, Youren, Cui Jiang, "A New Method of Fault Feature Extraction for Analog Circuits Based on the Most Fractional Fourier Transform[J],". Chinese Journal of Scientific Instrument, vol. 30, no. 5, pp. 997-1001, 2009. https://doi.org/10.3321/j.issn:0254-3087.2009.05.019
  8. Aminian, M. and F. Aminian, "Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor[J]," IEEE Transactions on Circuits and Systems II, vol. 47, no. 2, pp. 151-156, 2000. https://doi.org/10.1109/82.823545
  9. Cortes, C. and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, 3, pp. 273-297, 1995. https://doi.org/10.1007/BF00994018
  10. PCA I. Jolliffe, "Principal component analysis," Wiley Online Library, (2002).
  11. Xiao Yingqun, Feng Lianggui, He Yigang, "A fault diagnosis approach of analog circuit using wavelet based fractal analysis and kernel LDA[J]," Transactions of China Electrotechnical Society, vol. 27, no. 8, pp. 230-238(in Chinese), 2012.
  12. B. Scholkopf, A. J. Smola, K.R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput, vol. 10, pp. 1299-1319, 1998. https://doi.org/10.1162/089976698300017467
  13. M. S. Kearns, S. A. Solla, D. A. Cohn, "Kernel PCA and de-noising in feature spaces in Advances in Neural Information Processing Systems 11," MA, Cambridge: MIT Press, pp. 536-542, 1999.
  14. J. B. Tenenbaum, V. De Silva, J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000. https://doi.org/10.1126/science.290.5500.2319
  15. Roweis, S.T. and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding [J]," Science, vol. 290, no. 5500, pp. 2323-2326, 2000. https://doi.org/10.1126/science.290.5500.2323
  16. Polito, Marzia, and Pietro Perona, "Grouping and dimensionality reduction by locally linear embedding," pp. 2002, 1255-1262, 2002.
  17. DENG Y, YU C S. "Application of factor analysis and ELM in fault diagnosis of analog circuits [J]," Journal of Electronic Measurement & Instrumentation, vol. 30, no. 10, pp. 1512-1519, 2016.
  18. L.-Y. Zhao, L. Wang, and R.-Q. Yan, "Rolling bearing fault diagnosis based on wavelet packet decomposition and multi-scale permutation entropy," Entropy, vol. 17, no. 9, pp. 6447-6461, 2015. https://doi.org/10.3390/e17096447
  19. Xie Tao., "Research on Fault Diagnosis of Analog Circuits Based on Multiwavelet Packet, Neural Network and Optimization [D]," Hunan University, 2011.
  20. De Ridder, Dick, and Robert PW Duin., "Locally linear embedding for classification," Pattern Recognition Group, Dept. of Imaging Science & Technology, Delft University of Technology, Delft, The Netherlands, Tech. Rep. PH-2002-01, pp. 1-12, 2002.
  21. X. He, H. Wang, J. Lu, and W. Jiang, "Analog circuit fault diagnosis method based on preferred wavelet packet and ELM," Chinese Journal of Scientific Instrument, vol. 34, no. 11, pp. 2614-2619, 2013.
  22. W.J. Tian, Y. Geng, J.C. Liu, and L. Ai, "Support vector regression and immune clone selection algorithm for intelligent electronic circuit fault diagnosis in Proceedings of the Pacific-Asia Conerence on Circuits," Communications and System (PACCS '09), IEEE Computer Society, Chengdu, China, pp. 297-300, 2009.
  23. C. Gan, J. Wu, S. Yang, Y. Hu, W. Cao, "Wavelet packet decomposition-based fault diagnosis scheme for SRM drives with a single current sensor," IEEE Trans. Energy Convers, vol. 31, no. 1, pp. 303-313, Mar. 2016. https://doi.org/10.1109/TEC.2015.2476835
  24. Liu Xindong. Soft Fault Diagnosis of Analog Circuits Based on LLE and SVM., "System Simulation Techology and Its Application Conference," Changchun, Jilin, China, 2010.