SVM and PCA based fault classification approaches for complicated industrial process

Neurocomputing

Volumn 167, Issue , 2015, Pages 636-642

  Jing, Chen ^a   Hou, Jian ^a  

^a BOHAI UNIVERSITY   (China)

Author keywords

Fault classification; Principal component analysis; Support vector machine; Tennessee Eastman process

Indexed keywords

DATA HANDLING; MULTIVARIANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

COMPUTATIONAL EFFORT; FAULT CLASSIFICATION; INDUSTRIAL PROCESSS; MULTI FAULTS; MULTI-CLASSIFICATION; MULTIVARIATE STATISTICAL APPROACHES; TENNESSEE EASTMAN; TENNESSEE EASTMAN PROCESS;

SUPPORT VECTOR MACHINES;

ARTICLE; CALCULATION; INFORMATION PROCESSING; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL COMPUTING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SIMULATION; SUPPORT VECTOR MACHINE;

EID: 84952628377     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.082     Document Type: Article

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