[1]朱大奇,于盛林.电子电路故障诊断的神经网络数据融合算法[J].东南大学学报(自然科学版),2001,31(6):87-90.[doi:10.3969/j.issn.1001-0505.2001.06.021]
　Zhu Daqi,Yu Shenglin.Neural Network Data Fusion Algorithm of Circuit Fault Diagnosis[J].Journal of Southeast University (Natural Science Edition),2001,31(6):87-90.[doi:10.3969/j.issn.1001-0505.2001.06.021]
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电子电路故障诊断的神经网络数据融合算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
31
期数:
2001年第6期
页码:
87-90
栏目:
自动化
出版日期:
2001-11-20

文章信息/Info

Title:
Neural Network Data Fusion Algorithm of Circuit Fault Diagnosis
作者:
朱大奇1; 2;  于盛林1
1 南京航空航天大学测试工程系, 南京 210016; 2 安徽工业大学工业自动化系, 马鞍山 243002
Author(s):
Zhu Daqi1; 2;  Yu Shenglin1
1 Department of Testing and Measurement Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 2 Industrial Automation Department, Anhui University of Science And Technology, Ma’anshan 243002, China)
关键词:
神经网络;  数据融合;  故障诊断
Keywords:
neural networks;  data fusion;  fault diagnosis
分类号:
TP277;TH137.3
DOI:
10.3969/j.issn.1001-0505.2001.06.021
摘要:
针对模拟电路故障元件诊断的不确定性问题,将BP网络引入数据融合之中,结合模糊集合论,构造一模糊神经网络故障分类器,并将其应用于电子电路故障诊断之中.通过测试电子电路中被诊断元件的工作温度和工作电压这2个物理量,求出两传感器对各待诊断元件的故障隶属度,利用模糊BP网络故障分类器进行数据融合,得到融合的各待诊断元件的故障隶属度,从而确定故障元件,并通过单传感器诊断结果与融合诊断结果比较,说明多传感器融合的优越性.
Abstract:
In order to solve uncertain problem of circuit fault diagnosis, a fuzzy neural network fault classifier is designed based on BP neural network and fuzzy logical theory, and it is used in fault diagnosis. By measuring the temperature and voltage of circuit components, the membership function of two sensors to circuit component is obtained and the data fusion is made by using fuzzy BP neural network classifier. Thus the fusion fault membership function of all circuit components and the fault component can be found. By comparing the diagnosis results based on separate original data and fused date, it is shown that the latter is more accurate than the former in circuit fault recognition.

参考文献/References:

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[4] 韩静,陶云刚.基于D-S证据理论和模糊数学的多传感器数据融合算法.仪器仪表学报,2000,21(6):644～647
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备注/Memo

备注/Memo:
作者简介:朱大奇,男,1965年生,副教授,博士研究生.
基金项目:国家自然科学基金(59677021)、安徽省教委自然科学基金(2000JI174)资助项目.
更新日期/Last Update: 2001-11-20