| Titre : | Reconstruction-based contribution for process monitoring with kernel principal component analysis (2010) |
| Auteurs : | Carlos F. Alcala, Auteur ; S. Joe Qin, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 49 N° 17, Septembre 1, 2010) |
| Article en page(s) : | pp 7849–7857 |
| Note générale : | Chimie industrielle |
| Langues : | Anglais |
| Tags : | 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 |
