An improved FVS-KPCA method of fault detection on TE process

Proceedings - 2012 3rd International Conference on Digital Manufacturing and Automation, ICDMA 2012

Volumn , Issue , 2012, Pages 186-189

  Zhao, Xiaoqiang ^a,b   Wang, Xinming ^c   Yang, Wu ^a  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

^b Technology and Research Center of Gansu Manufacturing Infornation Engineering   (China)

^c Gansu Province Medical Science Institute   (China)

Author keywords

Fault Detection; Feature Vector Selection; Kernel Principal Component Analysis; Tennessee EastmanPprocess

Indexed keywords

COMPLEX NONLINEAR SYSTEM; DETECTION METHODS; FEATURE VECTOR SELECTION; FEATURE VECTORS; KERNEL MATRICES; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); SAMPLE SETS; TE PROCESS; TENNESSEE EASTMAN; TENNESSEE EASTMANPPROCESS;

CHEMICAL INDUSTRY; FAULT DETECTION; MANUFACTURE;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84868233120     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMA.2012.45     Document Type: Conference Paper

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