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Reconstruction-based contribution for process monitoring with kernel principal component analysis / Carlos F. Alcala in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7849–7857 Titre : Reconstruction-based contribution for process monitoring with kernel principal component analysis Type de document : texte imprimé Auteurs : Carlos F. Alcala, Auteur ; S. Joe Qin, Auteur Année de publication : 2010 Article en page(s) : pp 7849–7857 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Process  monitoring  Component  analysis. Résumé : This paper presents a new method for fault diagnosis based on kernel principal component analysis (KPCA). The proposed method uses reconstruction-based contributions (RBC) to diagnose simple and complex faults in nonlinear principal component models based on KPCA. Similar to linear PCA, a combined index, based on the weighted combination of the Hotelling’s T2 and SPE indices, is proposed. Control limits for these fault detection indices are proposed using second-order moment approximation. The proposed fault detection and diagnosis scheme is tested with a simulated CSTR process where simple and complex faults are introduced. The simulation results show that the proposed fault detection and diagnosis methods are effective for KPCA. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9018947 [article] Reconstruction-based contribution for process monitoring with kernel principal component analysis [texte imprimé] / Carlos F. Alcala, Auteur ; S. Joe Qin, Auteur . - 2010 . - pp 7849–7857. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7849–7857 Mots-clés : Process  monitoring  Component  analysis. Résumé : This paper presents a new method for fault diagnosis based on kernel principal component analysis (KPCA). The proposed method uses reconstruction-based contributions (RBC) to diagnose simple and complex faults in nonlinear principal component models based on KPCA. Similar to linear PCA, a combined index, based on the weighted combination of the Hotelling’s T2 and SPE indices, is proposed. Control limits for these fault detection indices are proposed using second-order moment approximation. The proposed fault detection and diagnosis scheme is tested with a simulated CSTR process where simple and complex faults are introduced. The simulation results show that the proposed fault detection and diagnosis methods are effective for KPCA. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9018947