Efficient recursive principal component analysis algorithms for process monitoring / Lamiaa M. Elshenawy in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 252–259 Titre : Efficient recursive principal component analysis algorithms for process monitoring Type de document : texte imprimé Auteurs : Lamiaa M. Elshenawy, Auteur ; Shen Yin, Auteur ; Amol S. Naik, Auteur Année de publication : 2010 Article en page(s) : pp. 252–259 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring Résumé : Principal component analysis (PCA) has been successfully applied in large scale process monitoring. However, classical PCA has some drawbacks: one of these aspects is the inability to deal with parameter-varying processes, where it interprets the natural changes in the process as faults, resulting in numerous false alarms. These false alarms threaten the credibility of the monitoring system. Therefore, recursive PCA (RPCA) algorithms are recommended. The most important challenge faced by these algorithms is the high computation costs, due to repeated eigenvalue decomposition (EVD) or singular value decomposition (SVD). Motivated by this issue, we present two RPCA algorithms that will greatly reduce the computation cost. The first algorithm is based on first-order perturbation analysis (FOP), which is a rank-one update of the eigenvalues and their corresponding eigenvectors of a sample covariance matrix. The second one is based on the data projection method (DPM), which is a simple and reliable approach for adaptive subspace tracking. The effectiveness of the presented RPCA algorithms is evaluated with an application of monitoring a nonisothermal continuous stirred tank reactor (CSTR) system. The results show the efficiency of these approaches compared to the classical PCA. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900720w [article] Efficient recursive principal component analysis algorithms for process monitoring [texte imprimé] / Lamiaa M. Elshenawy, Auteur ; Shen Yin, Auteur ; Amol S. Naik, Auteur . - 2010 . - pp. 252–259. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 252–259 Mots-clés : Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring Résumé : Principal component analysis (PCA) has been successfully applied in large scale process monitoring. However, classical PCA has some drawbacks: one of these aspects is the inability to deal with parameter-varying processes, where it interprets the natural changes in the process as faults, resulting in numerous false alarms. These false alarms threaten the credibility of the monitoring system. Therefore, recursive PCA (RPCA) algorithms are recommended. The most important challenge faced by these algorithms is the high computation costs, due to repeated eigenvalue decomposition (EVD) or singular value decomposition (SVD). Motivated by this issue, we present two RPCA algorithms that will greatly reduce the computation cost. The first algorithm is based on first-order perturbation analysis (FOP), which is a rank-one update of the eigenvalues and their corresponding eigenvectors of a sample covariance matrix. The second one is based on the data projection method (DPM), which is a simple and reliable approach for adaptive subspace tracking. The effectiveness of the presented RPCA algorithms is evaluated with an application of monitoring a nonisothermal continuous stirred tank reactor (CSTR) system. The results show the efficiency of these approaches compared to the classical PCA. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900720w

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Recursive fault detection and isolation approaches of time - varying processes / Lamiaa M. Elshenawy in Industrial & engineering chemistry research, Vol. 51 N° 29 (Juillet 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 29 (Juillet 2012) . - pp. 9812-9824 Titre : Recursive fault detection and isolation approaches of time - varying processes Type de document : texte imprimé Auteurs : Lamiaa M. Elshenawy, Auteur ; Hamdi. A. Awad, Auteur Année de publication : 2012 Article en page(s) : pp. 9812-9824 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Time varying system Failure detection Résumé : Recursive principal component analysis (RPCA) has gained significant attention as a monitoring tool for time-varying systems in recent years. The contribution of this article is the development of numerically efficient and memory-saving recursive fault detection and isolation (FDI) approaches for time-varying processes. The proposed approaches incorporate a recursive PCA based on a first-order perturbation (RPCA-FOP) analysis procedure and two recursive fault isolation methods. The proposed recursive fault isolation methods are the (i) recursive partial decomposition contribution (RPDC) and (ii) recursive diagonal contribution (RDC) methods. Four types of sensor faults, including bias, drifting, precision degradation, and complete failure, are simulated to test the proposed approaches. The utility of the proposed FDI approaches is demonstrated using a nonisothermal continuous stirred tank reactor (CSTR) system. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26184960 [article] Recursive fault detection and isolation approaches of time - varying processes [texte imprimé] / Lamiaa M. Elshenawy, Auteur ; Hamdi. A. Awad, Auteur . - 2012 . - pp. 9812-9824. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 29 (Juillet 2012) . - pp. 9812-9824 Mots-clés : Time varying system Failure detection Résumé : Recursive principal component analysis (RPCA) has gained significant attention as a monitoring tool for time-varying systems in recent years. The contribution of this article is the development of numerically efficient and memory-saving recursive fault detection and isolation (FDI) approaches for time-varying processes. The proposed approaches incorporate a recursive PCA based on a first-order perturbation (RPCA-FOP) analysis procedure and two recursive fault isolation methods. The proposed recursive fault isolation methods are the (i) recursive partial decomposition contribution (RPDC) and (ii) recursive diagonal contribution (RDC) methods. Four types of sensor faults, including bias, drifting, precision degradation, and complete failure, are simulated to test the proposed approaches. The utility of the proposed FDI approaches is demonstrated using a nonisothermal continuous stirred tank reactor (CSTR) system. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26184960

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