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A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA

Authors :
Jiaxu Wang
Yanwen Su
Xinmiao Lu
Yuhan Wei
Wu Qiong
Source :
Tehnički vjesnik, Volume 28, Issue 6, Tehnički Vjesnik, Vol 28, Iss 6, Pp 2121-2126 (2021)
Publication Year :
2021
Publisher :
Faculty of Mechanical Engineering in Slavonski Brod; Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; Faculty of Civil Engineering in Osijek, 2021.

Abstract

To obtain feature information of soft faults in non-linear analog circuits in a more effective way, this paper proposed a novel feature extraction method for soft faults in non-linear analog circuits based on Local Mean Decomposition-Generalized Fractal Dimension (LMD-GFD) and Kernel Principal Component Analysis (KPCA). First, the fault signals were subject to LMD, the features of each component signal were extracted by GFD for the first time, and a high-dimensional feature space was formed. Then, KPCA was employed to reduce the dimensionality of the high-dimensional feature space, and feature extraction was performed again; at last, KPCA and Support Vector Machine (SVM) were adopted to diagnose the faults. The experimental results showed that the proposed LMD-GFD-KPCA method had effectively extracted the features of the soft faults in the non-linear analog circuits, and it achieved a high diagnosis rate.

Details

Language :
English
ISSN :
18486339 and 13303651
Volume :
28
Issue :
6
Database :
OpenAIRE
Journal :
Tehnički vjesnik
Accession number :
edsair.doi.dedup.....c510bada932ae8d519013081c75d7155