Documents disponibles écrits par cet auteur

Multiway gaussian mixture model based multiphase batch process monitoring / Jie Yu in Industrial & engineering chemistry research, Vol. 48 N° 18 (Septembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 18 (Septembre 2009) . - pp. 8585–8594 Titre : Multiway gaussian mixture model based multiphase batch process monitoring Type de document : texte imprimé Auteurs : Jie Yu, Auteur ; S. Joe Qin, Auteur Année de publication : 2010 Article en page(s) : pp. 8585–8594 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Gaussian  mixture  model  Monitoring  approach Résumé : A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900479g [article] Multiway gaussian mixture model based multiphase batch process monitoring [texte imprimé] / Jie Yu, Auteur ; S. Joe Qin, Auteur . - 2010 . - pp. 8585–8594. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 18 (Septembre 2009) . - pp. 8585–8594 Mots-clés : Gaussian  mixture  model  Monitoring  approach Résumé : A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900479g

Multiway gaussian mixture model based multiphase batch process monitoring / Jie Yu in Industrial & engineering chemistry research, Vol. 48 N° 18 (Septembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 18 (Septembre 2009) . - pp. 8585–8594 Titre : Multiway gaussian mixture model based multiphase batch process monitoring Type de document : texte imprimé Auteurs : Jie Yu, Auteur ; S. Joe Qin, Auteur Année de publication : 2010 Article en page(s) : pp. 8585–8594 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Gaussian  mixture  model  Monitoring  approach Résumé : A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900479g [article] Multiway gaussian mixture model based multiphase batch process monitoring [texte imprimé] / Jie Yu, Auteur ; S. Joe Qin, Auteur . - 2010 . - pp. 8585–8594. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 18 (Septembre 2009) . - pp. 8585–8594 Mots-clés : Gaussian  mixture  model  Monitoring  approach Résumé : A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900479g

Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models / Gang Li in Industrial & engineering chemistry research, Vol. 49 N° 19 (Octobre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 19 (Octobre 2010) . - pp. 9175–9183 Titre : Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models Type de document : texte imprimé Auteurs : Gang Li, Auteur ; S. Joe Qin, Auteur ; Donghua Zhou, Auteur Année de publication : 2010 Article en page(s) : pp. 9175–9183 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Operations  industrial  processes Résumé : Statistical data-driven process monitoring is critical for efficient operations of industrial processes. However, deviations from normal regions in the process data may or may not lead to poor quality of products. This paper proposes a new combined index for detecting output-relevant faults, which affect the output data, and studies the output-relevant fault detectability based on total projection to latent structures (T-PLS). Given actual fault direction, fault-free data can be reconstructed and output-relevant part of fault magnitude can be estimated. Two new methods are derived to extract output-relevant fault subspace from faulty data. A simulation example and a case study on the Tennessee Eastman process are used to show the effectiveness of the proposed methods. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901939n [article] Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models [texte imprimé] / Gang Li, Auteur ; S. Joe Qin, Auteur ; Donghua Zhou, Auteur . - 2010 . - pp. 9175–9183. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 19 (Octobre 2010) . - pp. 9175–9183 Mots-clés : Operations  industrial  processes Résumé : Statistical data-driven process monitoring is critical for efficient operations of industrial processes. However, deviations from normal regions in the process data may or may not lead to poor quality of products. This paper proposes a new combined index for detecting output-relevant faults, which affect the output data, and studies the output-relevant fault detectability based on total projection to latent structures (T-PLS). Given actual fault direction, fault-free data can be reconstructed and output-relevant part of fault magnitude can be estimated. Two new methods are derived to extract output-relevant fault subspace from faulty data. A simulation example and a case study on the Tennessee Eastman process are used to show the effectiveness of the proposed methods. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901939n