Quality-related fault detection using linear and nonlinear principal component regression

Journal of the Franklin Institute

Volumn 353, Issue 10, 2016, Pages 2159-2177

  Wang, Guang ^a,d   Luo, Hao ^b   Peng, Kaixiang ^c  

^a BOHAI UNIVERSITY   (China)

^b UNIVERSITY OF DUISBURG ESSEN   (Germany)

^c UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^d HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

LEAST SQUARES APPROXIMATIONS; LINEAR SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; PROCESS MONITORING; REGRESSION ANALYSIS;

FAULT DETECTION SYSTEMS; KERNEL PARTIAL LEAST SQUARE (KPLS); KERNEL PRINCIPAL COMPONENT; LINEAR AND NONLINEAR PROBLEMS; LINEAR AND NONLINEAR SYSTEMS; NONLINEAR CHARACTERISTICS; PARTIAL LEAST SQUARE (PLS); PRINCIPAL COMPONENT REGRESSION;

FAULT DETECTION;

EID: 84963876941     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2016.03.021     Document Type: Article

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