Documents disponibles écrits par cet auteur

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

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