Steam turbine fault diagnosis method based on rough set theory and Bayesian network

被引：3

作者：

Han, Pu ^{[1
]}

Zhang, Deli ^{[1
]}

Zhou, Lihui ^{[1
]}

Jiao, Songming ^{[1
]}

机构：

[1] N China Elect Power Univ, Baoding 071003, Peoples R China

来源：

FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 1, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/FSKD.2007.532

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aiming at the uncertain problem in steam turbine fault diagnosis, a new method based on rough set theory and Bayesian network is proposed Simplify expert knowledge and reduce fault symptoms using reduction approach of rough set theory, the minimal diagnostic rules can be obtained. According to the minimal rules, complexity of Bayesian network structure and difficulties of fault symptom acquisition are largely decreased. At the same time, probability reasoning can be realized by Bayesian network. Finally, the correctness and effectiveness of this method are validated by the result of practical fault diagnosis examples.

引用

页码：419 / 422

页数：4

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