Dynamic multimode process modeling and monitoring using adaptive gaussian mixture models / Xiang Xie in Industrial & engineering chemistry research, Vol. 51 N° 15 (Avril 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 15 (Avril 2012) . - pp. 5497-5505 Titre : Dynamic multimode process modeling and monitoring using adaptive gaussian mixture models Type de document : texte imprimé Auteurs : Xiang Xie, Auteur ; Hongbo Shi, Auteur Année de publication : 2012 Article en page(s) : pp. 5497-5505 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Surveillance Modeling Résumé : For multimode processes, it is inevitable to encounter disturbances, such as equipment aging, catalyst deactivation, sensor drifting, reaction kinetics drifting, or adding new operating modes. The existing monitoring algorithms are established either for coping with multimode feature under time-invariant circumstance or for handling the time-varying problem of processes with single operating mode. The purpose of this article is to develop an effective modeling and monitoring approach for complex processes with both multimode and time-varying properties. We propose a novel adaptive monitoring scheme based on Gaussian Mixture Model (GMM). The new method is able to model different operating modes as well as trace process variations. The effectiveness and efficiency of the new method are validated by a numerical example and the Tennessee Eastman (TE) simulation platform in different scenarios. REFERENCE : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25815828 [article] Dynamic multimode process modeling and monitoring using adaptive gaussian mixture models [texte imprimé] / Xiang Xie, Auteur ; Hongbo Shi, Auteur . - 2012 . - pp. 5497-5505. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 15 (Avril 2012) . - pp. 5497-5505 Mots-clés : Surveillance Modeling Résumé : For multimode processes, it is inevitable to encounter disturbances, such as equipment aging, catalyst deactivation, sensor drifting, reaction kinetics drifting, or adding new operating modes. The existing monitoring algorithms are established either for coping with multimode feature under time-invariant circumstance or for handling the time-varying problem of processes with single operating mode. The purpose of this article is to develop an effective modeling and monitoring approach for complex processes with both multimode and time-varying properties. We propose a novel adaptive monitoring scheme based on Gaussian Mixture Model (GMM). The new method is able to model different operating modes as well as trace process variations. The effectiveness and efficiency of the new method are validated by a numerical example and the Tennessee Eastman (TE) simulation platform in different scenarios. REFERENCE : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25815828

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