基于局部图嵌入加权罚SVM的模拟电路故障诊断方法

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Abstract This paper proposes a method for analog IC diagnosis based on local graph embedding weighted-penalty support vector machine (SVM), to overcome the shortcomings of fault diagnosis method based on traditional SVM. A new type of SVM lying on data distribution is designed. Herein, maximize the inter-class margin of the entire data while optimizing local distribution of the data manifold, meanwhile, introduce the global data distribution information in error costs. The proposed method effectively combines the prior distribution information to improve the robustness and increase the diagnosis accuracy. The simulation results show the effectiveness of the algorithm.

Key words： Analog circuit fault diagnosis support vector machine data manifold

Published: 03 March 2016

PACS:	TP181
	TM930

Fund:国家自然科学基金重点资助项目（61203168）

Corresponding Authors: 廖剑男,1985年生,博士,主要研究方向为电路设计、测试与诊断。E-mail: 251250544@qq.com

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	Liao Jian
	Shi Xianjun
	Zhou Shaolei
	Xiao Zhicai

Cite this article:

Liao Jian,Shi Xianjun,Zhou Shaolei等. Analog Circuit Fault Diagnosis Based on Local Graph Embedding Weighted-Penalty SVM[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 28-35.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I4/28

[1] Peng M, Chi K T, Shen M, et al. Fault diagnosis of analog circuit using systematic tests based on data fusion[J]. Circuit Systems and Signal Processing, 2013, 32(2): 525-539.
[2] Aminian M, Aminian F. A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor[J]. IEEE Transactions on Instrumentation and Measure- ment, 2007, 56(5): 1546-1554.
[3] Aminian M, Aminian F. Neural-network based analog circuit fault diagnosis using wavelet transform as preprocessor[J]. IEEE Transactions on Circuit System II: Analog and Digital Signal Processing, 2000, 47(2): 151-156.
[4] Aminian M, Aminian F. Fault diagnosis of nonlinear circuits using neural networks with wavelet and fourier transforms as preprocessors[J]. Journal of Electronic Testing, 2001, 17(6): 471-481.
[5] Aminian M, Aminian F. Analog fault diagnosis of actual circuits using neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(3): 544-550.
[6] Huang Jian, Hu Xiaoguang, Yang Fan. Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker[J]. Measure- ment, 2011, 44(6): 1018-1027.
[7] Long Bing, Tian Shulin, Miao Qiang, et al. Research on features for diagnostics of filtered analog circuits based on LS-SVM[J]. IEEE Autotestcon, Systems Readiness Technology Conference, 2011, 124(1): 360-366.
[8] Long Bing, Tian Shulin, Wang Houjun. Diagnostics of filtered analog circuits with tolerance based on LS-SVM using frequency features[J]. Journal of Electronic Testing, 2012, 28(3): 291-300.
[9] 孙永奎, 陈光礻禹, 李辉. 基于自适应小波分解和SVM的模拟电路故障诊断[J]. 仪器仪表学报, 2008, 29(10): 2105-2109.
Sun Yongkui, Chen Guangju, Li Hui. Analog circuit fault diagnosis method using adaptive wavelet analysis and SVM[J]. Chinese Journal of Scientific Instrument, 2008, 29(10): 2105-2109.
[10] 宋国明, 王厚军, 姜书艳, 等. 最小生成树SVM的模拟电路故障诊断方法[J]. 电子科技大学学报, 2012, 41(3): 412-417.
Song Guoming, Wang Houjun, Jiang Shuyan, et al. Fault diagnosis approach for analog circuits using minimum spanning tree SVM[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 412-417.
[11] Xue Hui, Chen Songcan, Yang Qiang. Structural support vector machine[J]. Lecture Notes in Computer Science, 2008, 5263(1): 501-511.
[12] Lafon S, Keller Y, Coifman R R. Data fusion and multicue data matching by diffusion maps[J]. IEEE Transactions on Pattern Analysis and Machine Intel- ligence, 2006, 28(11): 1784-1797.
[13] 崔江, 王友仁. 一种新颖的基于混合故障字典方法的模拟故障诊断策略[J]. 电工技术学报, 2013, 28(4): 272-278.
Cui Jiang, Wang Youren. A novel strategy of analog fault diagnosis based on hybrid fault dictionaries[J]. Transactions of China Electrotechnical Society, 2013, 28(4): 272-278.
[14] 韩海涛, 马红光, 曹建福, 等. 基于非线性频谱特征及核主元分析的模拟电路故障诊断方法[J]. 电工技术学报, 2012, 27(8): 248-254.
Han Haitao, Ma Hongguang, Cao Jianfu, et al. Fault diagnosis method of analog circuits based on characteristics of the nonlinear frequency spectrum and KPCA[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 248-254.
[15] 唐静远, 师奕兵. 采用模糊支持向量机的模拟电路故障诊断新方法[J]. 电子测量与仪器学报, 2009, 23(6):7-12.
Tang Jingyuan, Shi Yibing. New method of analog circuit fault diagnosis using fuzzy support vector machine[J]. Journal of Electronic Measurement and Instrument, 2009, 23(6): 7-12.
[16] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995.
[17] 陶剑文, 王士同. 局部保留最大信息差ν-支持向量机[J]. 自动化学报, 2012, 38(1): 97-108.
Tao Jianwen, Wang Shitong. Locality-preserved maxi- mum information variance ν-support vector machine[J]. Acta Automatica Sinica, 2012, 38(1): 97-108.
[18] Wang Fei. A general learning framework using local and global regularization[J]. Pattern Recognition, 2010, 43(9): 3120-3129.
[19] Yan Shuicheng, Xu Dong, Zhang Benyu, et al. Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[20] Han Han, Wang Houjun, Tian Shulin, et al. A new analog circuit fault diagnosis method based on improved mahalanobis distance[J]. Journal of Elec- tronic Testing, 2013, 29(1): 95-102.
[21] 钟建林, 何友, 任献彬. 基于波形相似度的容差模拟电路软故障诊断[J]. 电工技术学报, 2012, 27(8): 222-229.
Zhong Jianlin, He You, Ren Xianbin. Soft fault diagnosis based on waveform similarity for tolerance analog circuit[J]. Transactions of China Electro- technical Society, 2012, 27(8): 222-229.
[22] Li Xifeng, Xie Yongle. Analog circuits fault detection using cross-entropy approach[J]. Journal of Electronic Testing, 2013, 29(1): 115-120.
[23] Tadeusiewicz M, Halgas S. Multiple soft fault diag- nosis of nonlinear circuits using the continuation method[J]. Journal of Electronic Testing, 2012, 28(4): 487-493.
[24] 罗慧, 王友仁, 崔江. 基于最优分数阶傅立叶变换的模拟电路故障特征提取新方法[J]. 仪器仪表学报, 2009, 30(5): 997-1001.
Luo Hui, Wang Youren, Cui Jiang. New approach to extract analog circuit fault features based on optimal fractional Fourier transforms[J]. Chinese Journal of Scientific Instrument, 2009, 30(5): 997-1001.