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

• Published in: Tehnički vjesnik Vol. 28; no. 6; pp. 2121 - 2126
• Main Authors: Lu, Xinmiao, Wang, Jiaxu, Wu, Qiong, Wei, Yuhan, Su, Yanwen
• Format: Journal Article Web Resource
• Language: English
• Published: Slavonski Baod University of Osijek 01.12.2021; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Analog circuits; Fault diagnosis; Fault Feature Extraction; Feature extraction; Fractal geometry; Generalized Fractal Dimension (GFD); Kernel functions; Kernel Principal Component Analysis (KPCA); Local Mean Decomposition (LMD); Nonlinear Analog Circuit; Principal components analysis; Support vector machines; China
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20210429033711

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.