Integration of rough set and neural network for application of generator fault diagnosis

被引：0

作者：

Su, WJ ^{[1
]}

Su, Y ^{[1
]}

Zhao, H ^{[1
]}

Zhang, XD ^{[1
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China

来源：

ROUGH SETS AND CURRENT TRENDS IN COMPUTING | 2004年 / 3066卷

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：

In the paper, integration of rough set and neural network for fault is put forward and used in generator fault diagnosis. At first, rough set theory is utilized to reduce attributes of diagnosis system. Set in accordance with the practical needs, optimized decision attribute set acts as the input of artificial neural network used for fault diagnosis, which has been used for Fengman hydroelectric power station and testified the feasibility of integration of rough set and neural network. Given enough data, this method could be popularized to other generators.

引用

页码：549 / 553

页数：5

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