[article]
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 |
in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7849–7857
[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 |
|