DocumentCode :
501310
Title :
Application of Synthetic Neural Network for Fault Diagnosis of Steam Turbine Flow Passage
Author :
Cao, Lihua ; Zhou, Yunlong ; Xu, Wei ; Li, Yong
Author_Institution :
Sch. of Energy & Power Eng., North China Univ. of Electr. Power, Beijing, China
Volume :
1
fYear :
2009
fDate :
6-7 June 2009
Firstpage :
62
Lastpage :
65
Abstract :
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.
Keywords :
backpropagation; fault diagnosis; neural nets; power engineering computing; steam turbines; backpropagation neural network; fault diagnosis; regenerative system fault; steam turbine flow passage; synthetic neural network; Computational intelligence; Computer applications; Computer networks; Condition monitoring; Fault diagnosis; Fluid flow measurement; Neural networks; Power generation; Temperature; Turbines; fault diagnosis; flow passage; relative internal efficiency; synthetic neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
Type :
conf
DOI :
10.1109/CINC.2009.110
Filename :
5231536
Link To Document :
بازگشت