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

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 Compo...

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Autores principales:	Xinmiao Lu*, Jiaxu Wang, Qiong Wu, Yuhan Wei, Yanwen Su
Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:	Fault Feature Extraction Generalized Fractal Dimension (GFD) Kernel Principal Component Analysis (KPCA) Local Mean Decomposition (LMD) Nonlinear Analog Circuit Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/c6061fe296b94018ac1ff1306c4a71ea
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id	oai:doaj.org-article:c6061fe296b94018ac1ff1306c4a71ea
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spelling	oai:doaj.org-article:c6061fe296b94018ac1ff1306c4a71ea2021-11-07T00:38:36ZA Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA1330-36511848-6339https://doaj.org/article/c6061fe296b94018ac1ff1306c4a71ea2021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/385034https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339To 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.Xinmiao Lu*Jiaxu WangQiong WuYuhan WeiYanwen SuFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articleFault Feature ExtractionGeneralized Fractal Dimension (GFD)Kernel Principal Component Analysis (KPCA)Local Mean Decomposition (LMD)Nonlinear Analog CircuitEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 2121-2126 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fault Feature Extraction Generalized Fractal Dimension (GFD) Kernel Principal Component Analysis (KPCA) Local Mean Decomposition (LMD) Nonlinear Analog Circuit Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Fault Feature Extraction Generalized Fractal Dimension (GFD) Kernel Principal Component Analysis (KPCA) Local Mean Decomposition (LMD) Nonlinear Analog Circuit Engineering (General). Civil engineering (General) TA1-2040 Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
description	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.
format	article
author	Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su
author_facet	Xinmiao Lu* Jiaxu Wang Qiong Wu Yuhan Wei Yanwen Su
author_sort	Xinmiao Lu*
title	A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
title_short	A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
title_full	A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
title_fullStr	A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
title_full_unstemmed	A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
title_sort	novel feature extraction method for soft faults in nonlinear analog circuits based on lmd-gfd and kpca
publisher	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate	2021
url	https://doaj.org/article/c6061fe296b94018ac1ff1306c4a71ea
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A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA

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