A novel approach of analog fault classification using a support vector machines classifier

• Autorzy: Cui J. , Wang Y.
• Języki publikacji: EN

Abstrakty

EN

In order to make the analog fault classification more accurate, we present a method based on the Support Vector Machines Classifier (SVC) with wavelet packet decomposition (WPD) as a preprocessor. In this paper, the conventional one-against-rest SVC is resorted to perform a multi-class classification task because this classifier is simple in terms of training and testing. However, this SVC needs all decision functions to classify the query sample. In our study, this classifier is improved to make the fault classification task more fast and efficient. Also, in order to reduce the size of the feature samples, the wavelet packet analysis is employed. In our investigations, the wavelet analysis can be used as a tool of feature extractor or noise filter and this preprocessor can improve the fault classification resolution of the analog circuits. Moreover, our investigation illustrates that the SVC can be applicable to the domain of analog fault classification and this novel classifier can be viewed as an alternative for the back-propagation (BP) neural network classifier.

Słowa kluczowe

EN

analog circuits; fault classification; support vector machines classifier; neural networks; wavelet packet decomposition

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2010
• Tom: Vol. 17, nr 4
• Strony: 561--581
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Cui J.

autor

Wang Y.

• College of Automation Engineering, Nanjing University of Aeronautics and Astronautcs, Nanjing City, Jiangsu Province, China,

Bibliografia

• [1] Catelani, M., Fort, A., Alippi, C. (2002). A fuzzy approach for soft fault detection in analog circuits. Meas., 32, 73-83.
• [2] Spina, R., Upadhyaya, S. (1997). Linear circuit fault diagnosis using neuro-morphic analyzers. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., 44(3),188-196.
• [3] Aminian, F., Aminian, M. (2001). Fault Diagnosis of Nonlinear Analog Circuits Using Neural Networks with Wavelet and Fourier Transforms as Preprocessors. J. Electron. Test.: Theory Appl., 17, 471-481.
• [4] El-Gamal, M.A., Abu El-Yazeed, M.F. (1999). A Combined Clustering and Neural Network Approach for Analog Multiple Hard Fault Classification. J. Electron. Test.: Theory Appl., 14, 207-217.
• [5] Yanghong, T., Yigang, H. (2008). A novel method for fault diagnosis of analog circuits based on WP and GPNN. Int. J. Electron., 95(5), 431-439.
• [6] 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), Oct., 1546-1554.
• [7] Fanni, A., Giua, A., Marchesi, M., Montisci, A. (1999). A Neural Network diagnosis Approach for analog circuits.Appl. Intell., 11(2), 169-186.
• [8] Yigang, H., Yanghong, T., Yichuang, S. (2004). Fault Diagnosis of Analog Circuits based on Wavelet Packets. In Proceedings of IEEE TENCON. Thailand, 267-270
• [9] Catelani, M., Fort, A. (2002). Soft Fault Detection and Isolation in Analog Circuits: Some Results and a Comparison between a Fuzzy Approach and Radial Basis Function Networks. IEEE Trans. Instrum. Meas., 51, (2), 196-202.
• [10] Salat, R., Osowski, S. (2003). Analog Filter Diagnosis Using Support Vector Machine. In Proceedings of ECCTD. Krakow, Poland, 421-424.
• [11] Siwek, K., Osowski, S., Markiewicz, T. (2006). Support Vector Machine for Fault Diagnosis in Electrical Circuits. In Proceedings of NORSIG. Iceland, 342-345.
• [12] Grzechca, D., Rutkowski, J. (2009). Fault Diagnosis in Analog Electronic Circuits - The SVM Approach. Metrol. Meas. Syst., 16(4), 583-598.
• [13] Vapnik, V.N. (1998). Statistical Learning Theory. New York: Wiley.
• [14] Hsu, C.W., Lin, C.J. (2002). A Comparison of Methods for Multi-class Support Vector Machines. IEEE Trans. Neural Networks, 13(2), 415-425.
• [15] Chapelle, O., Haffner, P., Vapnik, V.N. (1999). Support Vector Machines for histogram-based image classification. IEEE Trans. Neural Networks, 10(5), 1055-1064.
• [16] Takahashi, F., Abe, S. (2002). Decision-Tree-Based Multi-Class Support Vector Machines. In Proceedings ICONIP. Singapore, 1418-1422.
• [17] Burges, C.J.C. (1998). A Tutorial on Support Vector Machines For Pattern Recognition. Data Min. Knowl. Disc., 2(2), 121-167.
• [18] http://www.princeton.edu/~kung/ele571/571-MatLab/571svm/[as of Dec. 2008].