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Technical gazette, Vol. 28 No. 6, 2021.

Preliminary communication

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

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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.

Keywords

Fault Feature Extraction; Generalized Fractal Dimension (GFD); Kernel Principal Component Analysis (KPCA); Local Mean Decomposition (LMD); Nonlinear Analog Circuit

Hrčak ID:

265182

URI

https://hrcak.srce.hr/265182

Publication date:

7.11.2021.

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