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

页数：6

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