Modelling, Identification and Fault Diagnosis of a Simulated Model of an Industrial Gas Turbine

被引：0
|
作者：
Yousefi, Iman [1 ,2 ]
Khaloozadeh, Hamid [2 ]
Ashraf-Modarres, Ali [1 ]
机构：
[1] MAPNA Elect & Control Engn & Mfg Co MECO, Mapna Blvd,6th Km Malard Rd, Karaj, Iran
[2] KN Toosi Univ Technol, Elect Eng Fac, Tehran, Iran
来源：
2011 PROCEEDINGS OF THE 3RD CONFERENCE ON THERMAL POWER PLANTS (CTPP) | 2011年
关键词：
Fault Detection; Fault Diagnosis; Neural Networks; Identification Methods; Gas Turbine;
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 objective of this paper is to model, identify, and detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. An ARX model is used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model.
引用
收藏
页数：6
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