Application of neural networks in fault diagnosis of rotating machinery

被引：0

作者：

Qing, He ^{[1
]}

Dongmei, Du ^{[1
]}

机构：

[1] North China Elect Power Univ, Coll Energy & Power Engn, Minist Educ, Key Lab Condit Monitoring & Control Power Plant E, Beijing 102206, Peoples R China

来源：

Proceedings of the ASME Power Conference 2007 | 2007年

关键词：

rotating machinery; vibration; fault diagnosis; neural networks;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis method based on object-oriented artificial neural networks for more symptom domains is presented. The training patterns are constructed. A treatment for incomplete symptom domains and/or concurrent faults in diagnosing is given. Verification is carried out for the actual turbine-generator data with incomplete symptom domains.

引用

页码：279 / 282

页数：4

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