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

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Abstract For the characteristics that there existed much dimensions and big data volume in extracting fault signatures based on the model of nonlinear output frequency response function (NOFRF), a novel fault diagnosis method, which adopted kernel principal component analysis and multi-class support vector machine(KPCA-MSVM), is proposed to identify different fault states. Firstly, kernel principal component analysis(KPCA)is used to compress data dimension and eliminate nonlinearity among the variables. Secondly, multi-class support vector machine(MSVM)classifier is constructed, and the datum of all kinds of fault states, which were used to train MSVM classifier, are generated by Monte Carlo simulation with PSpice software. The trained MSVM classifier is used to identify different fault state. Via fault diagnosis for sallen-key band pass filter, the results indicate that KPCA-MSVM has good ability to identify and locate parametric faults for analog circuits, and has virtues of fast speed and high precision.

Key words： Nonlinear output frequency response functions kernel principal component analysis support vector machine fault signature fault diagnosis

Received: 04 March 2011 Published: 20 March 2014

PACS:

TN47/TP206.1

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	Han Haitao
	Ma Hongguang
	Cao Jianfu
	Zhang Jialiang

Cite this article:

Han Haitao,Ma Hongguang,Cao Jianfu等. 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.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I8/248

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