Tehnički vjesnik, Vol. 28 No. 6, 2021.
Prethodno priopćenje
https://doi.org/10.17559/TV-20210429033711
A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA
Xinmiao Lu*
; School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China
Jiaxu Wang
; School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China
Qiong Wu
; Heilongjiang Network Space Research Center, Harbin 150090, China
Yuhan Wei
; School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China
Yanwen Su
; School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China
Sažetak
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.
Ključne riječi
Fault Feature Extraction; Generalized Fractal Dimension (GFD); Kernel Principal Component Analysis (KPCA); Local Mean Decomposition (LMD); Nonlinear Analog Circuit
Hrčak ID:
265182
URI
Datum izdavanja:
7.11.2021.
Posjeta: 617 *