Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGWO

XIONG Kui; YUE Chang-xi; LIU Dong-mei; MEI Heng-rong

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Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGWO

更新时间：2026-09-04

- Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGWO
- Vol. 36, Issue 1, Pages: 16-21(2019)
- 作者机构：
  
  1. 中国电力科学研究院机构有限公司,湖北,武汉,430074
  2. 合肥工业大学电气与自动化工程学院,安徽,合肥,230009
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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熊魁, 岳长喜, 刘冬梅, 梅恒荣. 基于IGWO算法优化的SVM模拟电路故障诊断[J]. 微电子学与计算机, 2019, 36(1): 16-21.

XIONG Kui, YUE Chang-xi, LIU Dong-mei, et al. Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGWO[J]. 2019, 36(1): 16-21.
熊魁, 岳长喜, 刘冬梅, 梅恒荣. 基于IGWO算法优化的SVM模拟电路故障诊断[J]. 微电子学与计算机, 2019, 36(1): 16-21. DOI：

XIONG Kui, YUE Chang-xi, LIU Dong-mei, et al. Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGWO[J]. 2019, 36(1): 16-21. DOI：

摘要

为提高基于支持向量机(SVM)模拟电路故障诊断的准确率和优化效率

在灰狼优化(GWO)算法的基础上

通过引入非线性收敛因子、动态权重和边界变异策略

提出了一种改进灰狼优化(IGWO)算法优化SVM参数(IGWO-SVM)的改进型分类器.首先

在Sallen-Key带通滤波器和四运放双二次高通滤波器电路中采集故障样本

并对故障样本进行小波包特征提取; 然后

将特征提取后的样本划分为训练样本和测试样本

利用IGWO算法来优化SVM中的惩罚参数C和核参数γ

得到最优的SVM分类器模型; 最后

与其他算法优化的SVM分类器进行对比

结果表明IGWO-SVM分类器可以防止种群陷入局部最优

同时收敛速度有了较大提升.

Abstract

In order to improve the accuracy and optimization efficiency of analog circuit fault diagnosis based on support vector machine (SVM)

on the basis of gray wolf optimization (GWO) algorithm

this paper proposes a modified classifier that uses the improved gray wolf optimization (IGWO) algorithm to optimize the parameter of SVM (IGSA-SVM) by introducing the nonlinear convergence factor

dynamic weight and boundary variation strategy. Firstly

the fault samples are collected in the Sallen-Key bandpass filter circuit and four opamp biquad highpass filter circuit

and wavelet packet feature extraction is applied to fault samples. Then

feature-extracted samples are divided into training samples and test samples. The IGWO algorithm is used to optimize the penalty parameter C and the kernel parameters γ in SVM to obtain the optimal SVM classifier model. Finally

compared with SVM classifiers optimized by other algorithms

the results show that the IGWO-SVM classifier can prevent the population from falling into a local optimum

and the convergence speed has been greatly improved.

关键词

Keywords

references

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郭振洲, 刘然, 拱长青, 等.基于灰狼算法的改进研究[J].计算机应用研究, 2017, 33(12):3603-3606. DOI: 3603

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梅恒荣, 殷礼胜, 刘冬梅, 等.改进粒子群算法优化的SVM模拟电路故障诊断[J].电子测量与仪器学报, 2017, 31(8):1239-1246. DOI: 1239

刘美容, 张立玮.基于小波分解和模糊聚类的模拟电路软故障诊断[J].微电子学与计算机, 2014, 31(12):140-143. DOI: 140

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