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Two-dimensional dynamic principal component analysis with autodetermined support region / Yuan Yao in Industrial & engineering chemistry research, Vol. 48 N°2 (Janvier 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p.837–843 Titre : Two-dimensional dynamic principal component analysis with autodetermined support region Type de document : texte imprimé Auteurs : Yuan Yao, Auteur ; Yinghu Diao, Auteur ; Ningyun Lu, Auteur ; Junde Lu, Auteur ; Gao, Furong, Auteur Année de publication : 2009 Article en page(s) : p.837–843 Note générale : chemical engineering Langues : Anglais (eng) Mots-clés : Dynamics--Principal  Component  Analysis Résumé : Dynamics are inherent characteristics of batch processes. In some cases, such dynamics exist not only within a particular batch, but also from batch to batch. In previous work, two-dimensional dynamic principal component analysis (2-D-DPCA) has been developed to monitor 2-D dynamics. Support region determination is a key step in 2-D-DPCA modeling and monitoring of a batch process. A proper support region can ensure modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. In this work, an automatic method for support region determination is developed. This data-based method can be applied on different batch processes without prior process knowledge. Simulation shows that the developed method has good application potentials for both monitoring and fault diagnosis. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800825m [article] Two-dimensional dynamic principal component analysis with autodetermined support region [texte imprimé] / Yuan Yao, Auteur ; Yinghu Diao, Auteur ; Ningyun Lu, Auteur ; Junde Lu, Auteur ; Gao, Furong, Auteur . - 2009 . - p.837–843. chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p.837–843 Mots-clés : Dynamics--Principal  Component  Analysis Résumé : Dynamics are inherent characteristics of batch processes. In some cases, such dynamics exist not only within a particular batch, but also from batch to batch. In previous work, two-dimensional dynamic principal component analysis (2-D-DPCA) has been developed to monitor 2-D dynamics. Support region determination is a key step in 2-D-DPCA modeling and monitoring of a batch process. A proper support region can ensure modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. In this work, an automatic method for support region determination is developed. This data-based method can be applied on different batch processes without prior process knowledge. Simulation shows that the developed method has good application potentials for both monitoring and fault diagnosis. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800825m