Fault diagnosis of nonlinear analog circuit based on generalized frequency response function and LSSVM classifier fusion
type: Item
instance of: scholarly article
title: Fault diagnosis of nonlinear analog circuit based on generalized frequency response function and LSSVM classifier fusion
zbMATH Open document ID: 1459.93175
DOI: 10.1155/2020/8274570
author: Jialiang Zhang
published in: Mathematical Problems in Engineering
publication date: 2020-10-14T00:00:00Z
review text: Summary: For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 94C05
zbMATH DE Number: 7260878
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/8274570
OpenAlex ID: W3088723681
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description: scientific article; zbMATH DE number 7260878