Research on fault feature extraction for analog circuits
Pages 173 - 177
Abstract
In order to realize the accurate positioning and recognition effectively of the analog circuit, the feature extraction of fault information is an extremely important port. This arrival based on the experimental circuit which is designed as a failure mode to pick-up the fault sample set. We have chosen two methods, one is the combination of wavelet transform and principal component analysis, the other is the factorial analysis for the fault data's feature extraction, and we also use the extreme learning machine to train and diagnose the data, to compare the performance of these two methods through the accuracy of the diagnosis. The results of the experiment shows that the data which we get from the experimental circuit, after dealing with these two methods can quickly get the fault location.
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S.-W. Chen, S. Zhao, C. Wang, "A new analog circuit fault diagnosis approach based on GA-SVM," TENCON 2013, PP:1--4.
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S.-W. Chen, S. Zhao, C. Wang et al. A new analog circuit fault diagnosis approach based on GA-SVM{C}. 2013 IEEE Region 10 Conference (TENCON 2013), October 22-25 2013, Xi an, Shaanxi, China. 2013: 1--4.
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Published In
November 2016
235 pages
ISBN:9781450347907
DOI:10.1145/3015166
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Association for Computing Machinery
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Published: 21 November 2016
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ICSPS 2016
ICSPS 2016: 8th International Conference on Signal Processing Systems
November 21 - 24, 2016
Auckland, New Zealand
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ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;
Overall Acceptance Rate 46 of 83 submissions, 55%
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