Fault Diagnosis of Nonlinear Analog Circuit Based on Generalized Frequency Response Function and LSSVM Classifier Fusion

Jialiang Zhang

doi:10.1155/2020/8274570

Abstract

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.

Highlights

Analog circuits are widely used in various electronic systems and play a very important role
A modular fault diagnosis system for analog electronic circuits is designed based on wavelet transform and neural network [7]. e features are obtained by wavelet fractal analysis and kernel principal component analysis, and novel linear ridgelet network approach is used for analog circuit fault diagnosis [8]. e relationship between the input and output of analog circuit is very complex, which is often nonlinear. ere are still many problems in fault diagnosis of nonlinear analog circuits, especially in feature information acquisition and fault identification
Polynomial kernel function and Gaussian radial basis kernel function are used to construct two least square support vector machine (LSSVM) classifiers as subclassifiers. en, the two subclassifiers are fused to obtain the diagnosis result. e polynomial kernel function is a global kernel function, and the Gaussian radial basis kernel function is a local kernel function. erefore, this LSSVM fusion algorithm can take into account the global characteristics and local characteristics of the feature data obtained through the generalized frequency response functions of the nonlinear analog circuits

Summary

Introduction

Analog circuits are widely used in various electronic systems and play a very important role. In [22], a new fault diagnosis method for nonlinear analog circuit based on simplified estimation of GFRFs and hybrid-kernel least squares support vector machine is proposed. A new fault diagnosis method of nonlinear analog circuit is proposed based on generalized frequency response function and LSSVM classifier fusion algorithm. In order to simplify calculation, the sinusoidal signal is used as the input, and the time-domain measurements are used to directly estimate the GFRFs. After obtaining the generalized frequency response functions, their amplitudes of GFRFs are chosen as the feature parameters of analog circuits. After estimating the generalized frequency response functions of the nonlinear analog circuit by equation (14), their amplitudes are chosen as the feature parameters for fault diagnosis in this paper

Multifault Classifier Design Based on LSSVM Classifier Fusion

Experiment and Analysis

Findings

Conclusions