| Feature evaluation and extraction based on neural network in analog circuit fault diagnosis |
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| 作者单位： | Yuan Haiying（School of Automation Engineering, Univ. of Electronic Science and Technology of China, Chengdu 610054, P. R. China） ;
Chen Guangju（School of Automation Engineering, Univ. of Electronic Science and Technology of China, Chengdu 610054, P. R. China） ;
Xie Yongle（School of Automation Engineering, Univ. of Electronic Science and Technology of China, Chengdu 610054, P. R. China） ; |
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| 基金项目： | 国家自然科学基金;高等学校博士学科点专项科研项目 |
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| 摘 要： | Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The featureevaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.
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| 收稿时间： | 2 November 2005 |
Feature evaluation and extraction based on neural network in analog circuit fault diagnosis |
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| Authors: | Yuan Haiying Chen Guangju Xie Yongle |
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| Institution: | School of Automation Engineering, Univ. of Electronic Science and Technology of China, Chengdu 610054, P. R. China |
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| Abstract: | Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The featureevaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method. |
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| Keywords: | Fault diagnosis Feature extraction Analog circuit Neural network Principal component analysis |
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