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Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection / Mudassir M. Rashid in Industrial & engineering chemistry research, Vol. 51 N° 15 (Avril 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 15 (Avril 2012) . - pp. 5506-5514 Titre : Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection Type de document : texte imprimé Auteurs : Mudassir M. Rashid, Auteur ; Jie Yu, Auteur Année de publication : 2012 Article en page(s) : pp. 5506-5514 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Failure  detection  Surveillance  Independent  component  analysis  Modeling Résumé : For complex chemical processes with multiple operating conditions and inherent system uncertainty, conventional multivariate process monitoring techniques such as principal component analysis (PCA) and independent component analysis (ICA) are ill-suited because they are unable to characterize shifting modes and process uncertainty. In this article, a novel hidden Markov model (HMM) based ICA approach is proposed for process monitoring and fault detection. First the hidden Markov model is built from measurement data to estimate dynamic mode sequence. Further the localized ICA models are developed to characterize various operating modes adaptively. HMM based state estimation is then used to classify the monitored samples into the corresponding modes, and the HMM based I2 and SPE statistics are established for fault detection. The effectiveness of the proposed monitoring approach is demonstrated through the Tennessee Eastman Chemical process. The comparison of monitoring results shows that the proposed HMM-ICA approach is superior to the conventional ICA method and can achieve accurate detection of various types of process faults with minimized false alarms. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25815829 [article] Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection [texte imprimé] / Mudassir M. Rashid, Auteur ; Jie Yu, Auteur . - 2012 . - pp. 5506-5514. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 15 (Avril 2012) . - pp. 5506-5514 Mots-clés : Failure  detection  Surveillance  Independent  component  analysis  Modeling Résumé : For complex chemical processes with multiple operating conditions and inherent system uncertainty, conventional multivariate process monitoring techniques such as principal component analysis (PCA) and independent component analysis (ICA) are ill-suited because they are unable to characterize shifting modes and process uncertainty. In this article, a novel hidden Markov model (HMM) based ICA approach is proposed for process monitoring and fault detection. First the hidden Markov model is built from measurement data to estimate dynamic mode sequence. Further the localized ICA models are developed to characterize various operating modes adaptively. HMM based state estimation is then used to classify the monitored samples into the corresponding modes, and the HMM based I2 and SPE statistics are established for fault detection. The effectiveness of the proposed monitoring approach is demonstrated through the Tennessee Eastman Chemical process. The comparison of monitoring results shows that the proposed HMM-ICA approach is superior to the conventional ICA method and can achieve accurate detection of various types of process faults with minimized false alarms. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25815829

Multiway gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes / Jie Yu in Industrial & engineering chemistry research, Vol. 51 N° 40 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 40 (Octobre 2012) . - pp. 13227-13237 Titre : Multiway gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes Type de document : texte imprimé Auteurs : Jie Yu, Auteur Année de publication : 2012 Article en page(s) : pp. 13227-13237 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Software  sensor  Batchwise  Batchwise  Prediction  Partial  least  squares  Modeling Résumé : The predictive model based soft sensor technique has become increasingly important to provide reliable online measurements, facilitate advanced process control and optimization, and improve product quality in process industries. The conventional soft sensors are normally single-model based and thus may not be appropriate for processes with shifting operating phases or conditions and the underlying changing dynamics. In this study, a multiway Gaussian mixture model (MGMM) based adaptive kernel partial least-squares (AKPLS) method is proposed to handle online quality prediction of batch or semibatch processes with multiple operating phases. The three-dimensional measurement data are first preprocessed and unfolded into two-dimensional matrix. Then, the multiway Gaussian mixture model is estimated in order to identify and isolate different operating phases. Further, the process and quality measurements are separated into multiple segments corresponding to those identified phases, and the various localized kernel PLS models are built in the high-dimensional nonlinear feature space to characterize the shifting dynamics across different operating phases. Using Bayesian inference strategy, each process measurement sample of a new batch is classified into a particular phase with the maximal posterior probability, and thus, the local kernel PLS model representing the identical phase can be adaptively chosen for online quality variable prediction. The presented soft sensor modeling method is applied to a simulated multiphase penicillin fermentation process, and the computational results demonstrate that the proposed MGMM-AKPLS approach is superior to the conventional kernel PLS model in terms of prediction accuracy and model reliability. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26451472 [article] Multiway gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes [texte imprimé] / Jie Yu, Auteur . - 2012 . - pp. 13227-13237. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 40 (Octobre 2012) . - pp. 13227-13237 Mots-clés : Software  sensor  Batchwise  Batchwise  Prediction  Partial  least  squares  Modeling Résumé : The predictive model based soft sensor technique has become increasingly important to provide reliable online measurements, facilitate advanced process control and optimization, and improve product quality in process industries. The conventional soft sensors are normally single-model based and thus may not be appropriate for processes with shifting operating phases or conditions and the underlying changing dynamics. In this study, a multiway Gaussian mixture model (MGMM) based adaptive kernel partial least-squares (AKPLS) method is proposed to handle online quality prediction of batch or semibatch processes with multiple operating phases. The three-dimensional measurement data are first preprocessed and unfolded into two-dimensional matrix. Then, the multiway Gaussian mixture model is estimated in order to identify and isolate different operating phases. Further, the process and quality measurements are separated into multiple segments corresponding to those identified phases, and the various localized kernel PLS models are built in the high-dimensional nonlinear feature space to characterize the shifting dynamics across different operating phases. Using Bayesian inference strategy, each process measurement sample of a new batch is classified into a particular phase with the maximal posterior probability, and thus, the local kernel PLS model representing the identical phase can be adaptively chosen for online quality variable prediction. The presented soft sensor modeling method is applied to a simulated multiphase penicillin fermentation process, and the computational results demonstrate that the proposed MGMM-AKPLS approach is superior to the conventional kernel PLS model in terms of prediction accuracy and model reliability. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26451472

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

Nonlinear and non - gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach / Mudassir M. Rashid in Industrial & engineering chemistry research, Vol. 51 N° 33 (Août 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 33 (Août 2012) . - pp. 10910-10920 Titre : Nonlinear and non - gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach Type de document : texte imprimé Auteurs : Mudassir M. Rashid, Auteur ; Jie Yu, Auteur Année de publication : 2012 Article en page(s) : pp. 10910-10920 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Independent  component  analysis  Surveillance  Batchwise Résumé : Batch or semibatch process monitoring is a challenging task because of various factors such as strong nonlinearity, inherent time-varying dynamics, batch-to-batch variations, and multiple operating phases. In this article, a novel nonlinear and non-Gaussian dissimilarity method based on multiway kernel independent component analysis (MKICA) and multidimensional mutual information (MMI) is developed and applied to batch process monitoring and abnormal event detection. MKICA models are first built on the normal benchmark and monitored batches to characterize the nonlinear and non-Gaussian variable relationship of batch processes. Then, the kernel independent component (IC) subspaces are extracted from the benchmark and monitored batches. Further, a multidimensional mutual information based dissimilarity index is defined to quantitatively evaluate the statistical dependence between the benchmark and monitored subspaces through the moving-window strategy. With the corresponding control limit estimated from the kernel density function, the integrated MKICA―MMI index can be used to detect the abnormal events in dynamic batch processes. The effectiveness of the proposed batch process monitoring approach is demonstrated using the fed-batch penicillin fermentation process, and its performance is compared to that of the MKICA method. The computational results show that the presented dissimilarity approach is faster and more accurate in detecting different types of process faults. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26286465 [article] Nonlinear and non - gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach [texte imprimé] / Mudassir M. Rashid, Auteur ; Jie Yu, Auteur . - 2012 . - pp. 10910-10920. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 33 (Août 2012) . - pp. 10910-10920 Mots-clés : Independent  component  analysis  Surveillance  Batchwise Résumé : Batch or semibatch process monitoring is a challenging task because of various factors such as strong nonlinearity, inherent time-varying dynamics, batch-to-batch variations, and multiple operating phases. In this article, a novel nonlinear and non-Gaussian dissimilarity method based on multiway kernel independent component analysis (MKICA) and multidimensional mutual information (MMI) is developed and applied to batch process monitoring and abnormal event detection. MKICA models are first built on the normal benchmark and monitored batches to characterize the nonlinear and non-Gaussian variable relationship of batch processes. Then, the kernel independent component (IC) subspaces are extracted from the benchmark and monitored batches. Further, a multidimensional mutual information based dissimilarity index is defined to quantitatively evaluate the statistical dependence between the benchmark and monitored subspaces through the moving-window strategy. With the corresponding control limit estimated from the kernel density function, the integrated MKICA―MMI index can be used to detect the abnormal events in dynamic batch processes. The effectiveness of the proposed batch process monitoring approach is demonstrated using the fed-batch penicillin fermentation process, and its performance is compared to that of the MKICA method. The computational results show that the presented dissimilarity approach is faster and more accurate in detecting different types of process faults. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26286465

Nonlinear bioprocess monitoring using multiway kernel localized fisher discriminant analysis / Jie Yu in Industrial & engineering chemistry research, Vol. 50 N° 6 (Mars 2011) 

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