Analog fault diagnosis technology based on clonal selection and clustering

TIAN Xi-yu; WANG You-ren; CUI Jiang

doi:10.13873/j.1000-97872009.04.010

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Analog fault diagnosis technology based on clonal selection and clustering

更新时间：2025-09-11

- Analog fault diagnosis technology based on clonal selection and clustering
- Vol. 28, Issue 4, Pages: 43-46(2009)
- 作者机构：
  
  南京航空航天大学自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872009.04.010
  CLC： TN710
- Published：2009
- 稿件说明：
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[1]田锡宇,王友仁,崔江.基于克隆选择和聚类的模拟电路故障诊断技术[J].传感器与微系统,2009,28(04):43-46.

TIAN Xi-yu, WANG You-ren, CUI Jiang. Analog fault diagnosis technology based on clonal selection and clustering[J]. 2009, 28(4): 43-46.
[1]田锡宇,王友仁,崔江.基于克隆选择和聚类的模拟电路故障诊断技术[J].传感器与微系统,2009,28(04):43-46. DOI： 10.13873/j.1000-97872009.04.010.

TIAN Xi-yu, WANG You-ren, CUI Jiang. Analog fault diagnosis technology based on clonal selection and clustering[J]. 2009, 28(4): 43-46. DOI： 10.13873/j.1000-97872009.04.010.

摘要

提出了一种克隆选择和聚类的模拟电路故障诊断技术

该技术将克隆选择算法的全局寻优和模糊C—均值的局部寻优相结合

并利用模糊C—均值的目标函数去构造克隆选择算法的亲和力函数

可以减少特征空间中样本的相互重叠所产生的诊断不确定性

利于故障模式的定位。仿真结果和分析验证证明:该方法缩短了故障的分类器收敛时间

提高了故障诊断率。

Abstract

A new method of analog fault diagnosis technology based on clonal selection and clustering is proposed.It can not only combine global optimizing ability of clonal selection algorithm（CSA）and local optimizing ability fuzzy C—means（FCM）

but also utilize the target function of the FCM to construct the affinity function of the CSA.It can minimize the diagnosis uncertainty which is caused by overlapped samples and is advantage of fault location.Simulation results and analizing verify the proposed method curtails fault sorting convergence time and improves the fault diagnosis precision.

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references

基于小波分析和克隆选择算法的模拟电路故障诊断 [J]. 彭良玉,禹旺兵. 电工技术学报 . 2007(06)

基于SVM的模拟电子系统多故障诊断研究 [J]. 连可,王厚军,龙兵. 仪器仪表学报 . 2007(06)

基于高阶谱与支持向量机的电力电子电路故障诊断技术 [J]. 崔江,王友仁,刘权. 中国电机工程学报 . 2007(10)

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