Condition Monitoring of Equipment Using A Joint RSAR and Fuzzy ART Neural Network Method

被引：0

作者：

Gu, Jihai ^{[1
]}

Fan, Xianfeng ^{[2
]}

An, Ruoming ^{[3
]}

Tian, Ye ^{[1
]}

机构：

[1] Harbin Univ Commerce, Dept Mechanoelect Engn, Harbin 150028, Helongjiang, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Mech Engn, Chengdu 610054, Peoples R China

[3] Harbin Inst Technol, Dept Astron, Harbin 150001, Peoples R China

来源：

PACIIA: 2008 PACIFIC-ASIA WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION, VOLS 1-3, PROCEEDINGS | 2008年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Working conditions are monitoring parameters are huge and neural network learing time too long in the condition monitoring of multi word condition equipment. To improve monitoring efficiency, a joint Rough Set Attribute Reduction (RSAR) and Fuzzy ART (Adaptive Resonance Theory) neural network method is proposed in this study. The dimension of an input vector to Fuzzy ART neural networks can be reduced through RSAR. The updated vectors are used to train Fuzzy ART neural networks. An example is investigated to evaluate the proposed method in this study. Analysis results indicate that the proposed method can save great learning time without losing monitoring capability. Additionally, sensor abnormality and signal transmission issues may be detected as well.

引用

页码：1019 / +

页数：2

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