Abstract
Most researchers have used the optimal wavelet coefficients or wavelet energy indicators from the time-domain response of analog circuits to train support vector machines (SVMs) to diagnose faults. In this study, we have proposed two kinds of feature vectors from frequency response data of a filter system to train least squares SVM (LS-SVM) to diagnose faults. The first is defined as the conventional frequency feature vector, which includes the center frequency and the maximum frequency response. The second is a new wavelet feature vector that is composed of the mean and standard deviation of wavelet coefficients. Different feature vectors’ combination and normalization are also discussed in the paper. The results from the simulation data and the real data for two filters showed the following: (1) The proposed method has better diagnostic accuracy than the traditional methods that were based only on the optimal wavelet coefficients or wavelet energy indicators. (2) The diagnostic accuracies using the combined feature vectors were better than those using only the conventional frequency feature vectors or wavelet feature vectors. (3) The best accuracy from using the conventional frequency feature vectors was better than that from using wavelet feature vectors. The proposed method can be extended to diagnostics of other analog circuits that are determined by their frequency characteristics.
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References
Aminian M, Aminian F (2000) Neural-network based analog circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circuits Syst II, Exp Briefs 47(2):151–156
Aminian M, Aminian F (2001) Fault diagnosis of nonlinear circuits using Neural Networks with wavelet and Fourier transforms as preprocessors. JETTA 17:471–481
Aminian M, Aminian F (2002) Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas 51(3):544–550
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
Chen C, Brown D, Sconyers C et al (2010) A .NET framework for an integrated fault diagnosis and failure prognosis architecture IEEE Autotestcon, Orlando, FL, Sept. 13–16, 2010: 1–6
Chen C, Zhang B, Vachtsevanos G, Orchard M (2011) Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Trans Ind Electron 58(9):4353–4364
Cui J, Wang YR (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44:281–289
Hsu CW, Lin CJ (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw 13(2):415–425
Huang J, Hu XG, Yang F (2011) Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement 44:1018–1027
John K (2000) OrCAD Pspice and circuit analysis, 4th edn. Prentice Hall
Long B, Tian SL, Miao Q, Pecht M (2011) Research on features for diagnostics of filtered analog circuits based on LS-SVM. IEEE Autotestcon. Baltimore, MD, Step.12–15, 2011: 360–366
Long B, Tian SL, Wang HJ (2008) Least squares support vector machine based analog circuit fault diagnosis using wavelet transform as preprocessor. ICCCAS 2008. doi:10.1109/ICCCAS.2008.4657943
Mao XB, Wang LH, Li CX (2008) SVM classifier for analog fault diagnosis using fractal features. IEEE second international symposium on intelligent information technology application. doi:10.1109/IITA.2008.249
Prithviraj K (2005) Fault diagnosis of analog integrated circuits. Springer, Netherlands
Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific, Singapore
Tang L, Hu Y, Lin T, Chen Y (2010) Analog circuit fault diagnosis based on fuzzy support vector machine and kernel density estimation. 2010 3rd International Conference on Advanced Computer Theory and Engineering. doi:10.1109/ICACTE.2010.5579303
Williams A, Taylor F (2006) Electronic filter design handbook (fourth edition). McGraw-Hill, New York
Yang CL, Tian SL, Long B (2011) Methods of handling the tolerance and test-point selection problem for analog circuit fault diagnosis. IEEE Trans Instrum Meas 60(1):176–185
Zhang Y, Wei XY, Jiang HF (2008) One-class classifier based on SBT for analog circuit fault diagnosis. Measurement 41:371–380
Zuo L, Hou LG, Zhang W, Wu WC (2010) Applying wavelet support vector machine to analog circuit fault diagnosis. 2010 Second International Workshop on Education Technology and Computer Science. doi:10.1109/ETCS.2010.255
Acknowledgment
This work was supported in part by National Natural Science Foundation of China under Grants 61071029, 60934002 and 51175443, and in part by the Fundamental Research Funds for the Central Universities (E022050205) and University of Electronic Science and Technology of China (Y02018023601059). The authors would like to thank all anonymous reviewers’ valuable comments on this paper.
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Long, B., Tian, S. & Wang, H. Diagnostics of Filtered Analog Circuits with Tolerance Based on LS-SVM Using Frequency Features. J Electron Test 28, 291–300 (2012). https://doi.org/10.1007/s10836-011-5275-y
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DOI: https://doi.org/10.1007/s10836-011-5275-y