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Estimation of instrument variance and bias using bayesian methods / Ruben Gonzalez in Industrial & engineering chemistry research, Vol. 50 N° 10 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6229-6239 Titre : Estimation of instrument variance and bias using bayesian methods Type de document : texte imprimé Auteurs : Ruben Gonzalez, Auteur ; Biao Huang, Auteur ; Fangwei Xu, Auteur Année de publication : 2011 Article en page(s) : pp. 6229-6239 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Bias  Instruments Résumé : Imprecision of sensors is one of the main causes of poor control and process performance. Often, instrument measurement bias and variance change over the time and online calibration/re-estimation is necessary. Originated from a real industrial application problem, this paper proposed a Bayesian approach to determine the inconsistency of sensors, based on mass-balance principles. A mass-balance factor model is then introduced, where the factor analysis method is used to determine initial values for estimating instrument noise and process disturbance variance. Because of the structural constraint of mass-balance equations, a gray-box estimation procedure must be adopted for which Bayesian network estimation via the expectation-maximization (EM) algorithm is a very suitable method. Therefore, this paper uses factor analysis to determine the initial values, and, afterward, estimates process and sensor variance by means of Bayesian estimation. After estimating the process and instrument variance, the process steady state and instrument bias can be similarly estimated. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158920 [article] Estimation of instrument variance and bias using bayesian methods [texte imprimé] / Ruben Gonzalez, Auteur ; Biao Huang, Auteur ; Fangwei Xu, Auteur . - 2011 . - pp. 6229-6239. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6229-6239 Mots-clés : Bias  Instruments Résumé : Imprecision of sensors is one of the main causes of poor control and process performance. Often, instrument measurement bias and variance change over the time and online calibration/re-estimation is necessary. Originated from a real industrial application problem, this paper proposed a Bayesian approach to determine the inconsistency of sensors, based on mass-balance principles. A mass-balance factor model is then introduced, where the factor analysis method is used to determine initial values for estimating instrument noise and process disturbance variance. Because of the structural constraint of mass-balance equations, a gray-box estimation procedure must be adopted for which Bayesian network estimation via the expectation-maximization (EM) algorithm is a very suitable method. Therefore, this paper uses factor analysis to determine the initial values, and, afterward, estimates process and sensor variance by means of Bayesian estimation. After estimating the process and instrument variance, the process steady state and instrument bias can be similarly estimated. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158920