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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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

Received: 10 September 2010 Published: 20 March 2014

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TM206

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	Xiao Yingqun
	Feng Lianggui
	He Yigang

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Xiao Yingqun,Feng Lianggui,He Yigang. A Fault Diagnosis Approach of Analog Circuit Using Wavelet-Based Fractal Analysis and Kernel LDA[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 230-238.

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

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