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A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
- 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.
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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.
- Subjects :
- Fault Feature Extraction
Generalized Fractal Dimension (GFD)
Kernel Principal Component Analysis (KPCA)
Local Mean Decomposition (LMD)
Nonlinear Analog Circuit
business.industry
Computer science
Feature extraction
General Engineering
Pattern recognition
Engineering (General). Civil engineering (General)
Computer Science::Hardware Architecture
Nonlinear analog circuits
Artificial intelligence
TA1-2040
business
Subjects
Details
- Language :
- English
- ISSN :
- 18486339 and 13303651
- Volume :
- 28
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Tehnički vjesnik
- Accession number :
- edsair.doi.dedup.....c510bada932ae8d519013081c75d7155