Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier

被引：0
|
作者：
Nashalji, Mostafa Noruzi [1 ]
Shoorehdeli, Mandi Aliyari [1 ]
Teshnehlab, Mohammad [1 ]
机构：
[1] KN Toosi Univ Technol, Fac Elect Engn, Intelligent Syst Lab, Tehran, Iran
来源：
SOFT COMPUTING IN INDUSTRIAL APPLICATIONS - ALGORITHMS, INTEGRATION, AND SUCCESS STORIES | 2010年 / 75卷
关键词：
PRINCIPAL COMPONENT ANALYSIS; PATTERN-RECOGNITION; GENETIC ALGORITHM; DIAGNOSIS; SELECTION; NUMBER; VARIANCE; SYSTEMS; KICA; ICA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm. This method determines main Principal Components can be used to detect fault during the operation of industrial process by neural classifier. This technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman chemical.
引用
收藏
页码：41 / 50
页数：10
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