Fault detection for a class of industrial processes based on recursive multiple models

Neurocomputing

Volumn 169, Issue , 2015, Pages 430-438

  Yong, Gao ^a   Xin, Wang ^b   Zhenlei, Wang ^a  

^a School of Information Science and Engineering   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Multiple models; RKPCA; SVDD; Time varying

Indexed keywords

ARTIFICIAL INTELLIGENCE; CRACKS; DATA DESCRIPTION; ETHYLENE; FAULT DETECTION; MONITORING; PETROCHEMICAL PLANTS; PETROCHEMICALS; PROCESS MONITORING;

ETHYLENE CRACKING FURNACE; PETROCHEMICAL PROCESS; PRINCIPAL COMPONENTS; RKPCA; SUPPORT VECTOR DATA DESCRIPTION; SVDD; TIME VARYING; TIME-VARYING PROCESS;

PRINCIPAL COMPONENT ANALYSIS;

ETHYLENE;

ARTICLE; CLASSIFICATION ALGORITHM; FUZZY SYSTEM; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PETROCHEMICAL INDUSTRY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS MONITORING; SUPPORT VECTOR MACHINE;

EID: 84938201172     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.08.107     Document Type: Article

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