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
收藏
页码：279 / 282
页数：4
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