Nonlinear process monitoring based on maximum variance unfolding projections

Expert Systems with Applications

Volumn 36, Issue 8, 2009, Pages 11332-11340

  Shao, Ji Dong ^a   Rong, Gang ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Dimensionality reduction; Kernel matrix learning; Linear regression; Maximum variance unfolding projections; Process monitoring

Indexed keywords

COMPUTATIONAL COSTS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; FAULT DETECTION AND IDENTIFICATION; FAULTY PROCESS; KERNEL FUNCTION; KERNEL MATRICES; KERNEL MATRIX LEARNING; KERNEL PRINCIPAL COMPONENT ANALYSIS; MANIFOLD LEARNING; MAXIMUM VARIANCE; MAXIMUM VARIANCE UNFOLDING PROJECTIONS; MONITORING METHODS; NONLINEAR PROCESS; NONLINEAR PROCESS MONITORING; OFF-LINE MODELING; ONLINE MONITORING; REDUCED SPACE; SIMULATION RESULT; TENNESSEE EASTMAN PROCESS; TRAINING SAMPLE;

EDUCATION; FAULT DETECTION; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING; REGRESSION ANALYSIS;

MATRIX ALGEBRA;

EID: 67349265618     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.042     Document Type: Article

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