基于小波分形和核判别分析的模拟电路故障诊断

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摘要提出了采用小波分形分析和核判别分析作为预处理器来实行特征提取的神经网络模拟电路故障诊断方法。这个诊断方法采用小波分形分析方法首先获取了故障响应信号的小波分形维特征, 然后采用核判别分析进一步实施特征提取, 最后将所获得的最优特征模式作为神经网络分类器的输入以进行故障诊断。仿真结果表明, 本文提出的预处理方法能很好地获取故障响应信号的本质特征, 并表现出了比其他特征提取方法更好的性能。并且, 由此所构建的神经网络不但具有小的网络结构, 而且能取得高的故障诊断正确率。

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	肖迎群
	冯良贵
	何怡刚

关键词 ：模拟电路, 故障诊断, 特征提取, 小波分形分析, 核判别分析

Abstract：A neural-network fault diagnosis approach utilizing wavelet-based fractal analysis method and kernel linear discriminant analysis(KLDA) as preprocessors is proposed. The diagnostic approach preprocesses the fault response signals in such a way that the wavelet-based fractal analysis obtains the fractal-dimension features of fault response signals, and KLDA further extracts the optimal features used as the inputs to neural-network classifier. The simulation results show that the proposed method can acquire the essential features of fault response signals and display better performance than other methods. Furthermore, the resulting neural networks not only have the small structures but also can achieve high accuracy of fault diagnosis.

Key words： Analog circuits fault diagnosis feature extraction wavelet-based fractal analysis kernel LDA

收稿日期: 2010-09-10 出版日期: 2014-03-20

PACS:

TM206

作者简介: 肖迎群男, 1975年生, 博士后, 主要研究方向为模式识别, 机器学习, 模拟电路与系统的测试及诊断等。冯良贵男, 1968年生, 教授, 博士生导师, 主要研究方向为代数结构理论与应用, 机器学习理论与方法和高维数据的统计分析等。

引用本文:

肖迎群, 冯良贵, 何怡刚. 基于小波分形和核判别分析的模拟电路故障诊断[J]. 电工技术学报, 2012, 27(8): 230-238. Xiao Yingqun, Feng Lianggui, He Yigang. A Fault Diagnosis Approach of Analog Circuit Using Wavelet-Based Fractal Analysis and Kernel LDA. Transactions of China Electrotechnical Society, 2012, 27(8): 230-238.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2012/V27/I8/230

[1] Bandler J W, Salama A E. Fault diagnostic of analog circuits[J]. Proc. IEEE, 1985, 73(8): 1279-1325.
[2] Spina R, Upadhyaya S. Linear circuit fault diagnosis using neuromorphic analyzers[J]. IEEE Transactions on Circuits and Systems II, 1997, 44( 3): 188-196.
[3] Aminian M, Aminian F. Neural-network based analog- circuit fault diagnosis using wavelet transform as preprocessor[J]. IEEE Trans. Circuits. Syst.-II, 2000, 47(2): 151-156.
[4] Aminian F, Aminian M, Collins H W. Analog fault diagnosis of actual circuits using neural networks [J]. IEEE Trans. Instrum. Meas., 2002, 51(3): 544-550.
[5] He Y, Tan Y, Sun Y. Wavelet neural network approach for fault diagnosis of analog circuits[J].IEE Proc. Circuits Devices Syst., 2004, 151(4): 379-384.
[6] 肖迎群, 何怡刚. 基于脊波网络的模拟电路故障诊断[J]. 电工技术学报, 2010, 25(6): 155-162.
Xiao Yingqun, He Yigang. A fault diagnosis method of analog circuit based on ridgelet network[J]. Transactions of China Electrotechnical Society, 2010. 25(6): 155-162.
[7] Xiao Yingqun, He Yigang. A linear ridgelet network approach for fault diagnosis of analog circuit[J]. Sci China Ser F-Inf. Sci, 2010, 53(11): 2251-2264.
[8] Yuan Lifen, He Yigang, Huang Jiaoying, et al. A new neural-network-based fault diagnosis approach for analog circuits by using Kurtosis and entropy as a preprocessor[J]. IEEE Trans. Instrum. Meas., 2010, 59(3): 586-595.
[9] Falconer J. Fractal geometry-mathematical foundati- ons and applications[M]. West Sussex: John Wiley and Sons, 2003.
[10] Kartz M. Fractals and the analysis of waveforms[J]. Comput. Biol. Med., 1988, 18(3):145-156.
[11] Rosana Esteller, George Vachtsevanos, Javier Echauz, et al. A comparison of waveform fractal dimension algorithms[J]. IEEE Transactions on Circuitd and Systems-Ⅰ: Fundamental Theory and Applications, 2001, 48(2): 177-183.
[12] Raghavendra B S, Narayana Dutt D. A note on fractal dimensions of biomedical waveforms[J]. Comput. Biol. Med., 2009, 39(9): 1006-1012.
[13] Bishop C M. Neural networks for pattern recognition [M]. New York: Oxford Univ. Press, 1995.
[14] Fukunaga K. Introduction to statistical pattern recognition[M]. New York: Academic, 1990.
[15] Schölkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Comput., 1998, 10 (3): 1299-1319.
[16] Schölkopf B, Smola A, Learning with kernels[M]. Cambridge, MA: MIT Press, 2002.
[17] Lu Juwei, Plataniotis K N, Venetsanopoulos A N. Face recognition using kernel direct discriminant analysis algorithms[J]. IEEE Trans. on Neural Networks, 2003, 14 (1) : 117-125.
[18] Lu Juwei, Plataniotis K N, Venetsanopoulos A N. Face recognition using LDA-based algorithms[J], IEEE Trans. on Neural Networks, 2003, 14 (1): 195-200.
[19] Strang G, Nguyen T. Wavelet and filter banks[M]. Cambridge, MA: Wellesley-Cambridge Press, 1996.
[20] Wang Lei, Chan Kap Luk, Xue Ping, et al. A Kernel-induced space selection approach to model selection in KLDA [J]. IEEE Trans. on Neural Networks, 2008, 19 (12): 2116-2131.
[21] Wang Lei. Feature selection with kernel class separability[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2008, 30 (9): 1534-1546.
[22] Deliyannis T, Sun Y, Fidler J K. Continuous-time active filter[M]. Boca Raton, FL: CRC Press, 1999.