Application of Synthetic Neural Network for Fault Diagnosis of Steam Turbine Flow Passage

被引：2
|
作者：
Cao, Lihua [1 ]
Zhou, Yunlong [2 ]
Xu, Wei [2 ]
Li, Yong [2 ]
机构：
[1] North China Univ Elect Power, Sch Energy & Power Engn, Beijing, Peoples R China
[2] Northeast Dianli Univ, Sch Energy & Mech Engn, Jian, Jiangxi, Peoples R China
来源：
PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL I | 2009年
关键词：
steam turbine; flow passage; fault diagnosis; relative internal efficiency; synthetic neural network;
D O I：
10.1109/CINC.2009.110
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is difficult to determine the normal value for fault diagnosis of steam turbine flow passage, because the flow passage fault or regenerative system fault may result in the decrease of the relative internal efficiency of steam turbine. Based on analyzing the feature of flow passage and regenerative system, the flow passage condition of steam turbine can be evaluated by relative internal efficiency of stage groups. According to various effect factors, the methods to determine the normal value of relative internal efficiency of stage groups is proposed in this paper, which include application of synthetic neural network. Compared the measured values with these normal values, the operating condition of steam turbine flow passage can be evaluated and the detailed reasons of fault can be diagnosed.
引用
收藏
页码：62 / +
页数：2
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