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

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