Dynamic bayesian approach for control loop diagnosis with underlying mode dependency / Fei Qi in Industrial & engineering chemistry research, Vol. 49 N° 18 (Septembre 2010)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8613–8623 Titre : Dynamic bayesian approach for control loop diagnosis with underlying mode dependency Type de document : texte imprimé Auteurs : Fei Qi, Auteur ; Biao Huang, Auteur Année de publication : 2010 Article en page(s) : pp. 8613–8623 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Dynamic bayesian Résumé : In this article, first, a hidden Markov model is built to address the temporal mode dependency problem in control loop diagnosis. A data-driven algorithm is developed to estimate the mode transition probability. The new solution to mode dependency is then further synthesized with the solution to evidence dependency to develop a recursive autoregressive hidden Markov model for online control loop diagnosis. When both the mode and evidence transition information sets are considered, the temporal information is effectively synthesized under the Bayesian framework. A simulated distillation column example and a pilot-scale experiment example are investigated to demonstrate the ability of the proposed diagnosis approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100058y [article] Dynamic bayesian approach for control loop diagnosis with underlying mode dependency [texte imprimé] / Fei Qi, Auteur ; Biao Huang, Auteur . - 2010 . - pp. 8613–8623. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8613–8623 Mots-clés : Dynamic bayesian Résumé : In this article, first, a hidden Markov model is built to address the temporal mode dependency problem in control loop diagnosis. A data-driven algorithm is developed to estimate the mode transition probability. The new solution to mode dependency is then further synthesized with the solution to evidence dependency to develop a recursive autoregressive hidden Markov model for online control loop diagnosis. When both the mode and evidence transition information sets are considered, the temporal information is effectively synthesized under the Bayesian framework. A simulated distillation column example and a pilot-scale experiment example are investigated to demonstrate the ability of the proposed diagnosis approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100058y

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