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A probabilistic framework for real-time performance assessment of inferential sensors
Affiliation:1. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2G6;2. Syncrude Canada Ltd., Fort McMurray, Alberta, Canada T9H 3L1;3. Suncor Energy, Fort McMurray, Alberta, Canada;1. Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace Engineering, 101 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853-3801, USA;2. Center for Applied Mathematics, 657 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853-3801, USA;1. Department of Pharmacy & Medical Laboratory, Ya’an Vocational College, Ya’an 625000, P. R. China;2. Department of Traditional Chinese Medicine, Lianyungang TCM Branch of Jiangsu Union Technical Institute, Lianyungang 222007, P. R. China;1. Department of Statistical Science, University College London, United Kingdom;2. Department of Computer and Systems Sciences, Stockholm University, Sweden;1. Posgrado del Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P. 62490, Cuernavaca, Morelos, Mexico;2. Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P. 62490, Cuernavaca, Morelos, Mexico;3. CONACYT-Instituto de Ingeniería-UNAM, Ciudad de México, Mexico;4. CONACYT-Centro Nacional de Investigación y Desarrollo Tecnológico. Int. Internado Palmira S/N, Palmira C.P. 62490, Cuernavaca, Morelos, Mexico;5. Centro de Investigación en Ingeniería y Ciencias Aplicadas-Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Col. Chamilpa, C.P. 62209, Cuernavaca, Morelos, Mexico
Abstract:A definition for the reliability of inferential sensor predictions is provided. A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The main focus is on characterizing the effect of operating space on the reliability of inferential sensor predictions. A holistic, quantitative measure of the reliability of the inferential sensor predictions is introduced. A methodology is provided to define objective prior probabilities over plausible classes of reliability based on the total misclassification cost. The real-time performance assessment of multi-model inferential sensors is also discussed. The application of the method does not depend on the identification techniques employed for model development. Furthermore, on-line implementation of the method is computationally efficient. The effectiveness of the method is demonstrated through simulation and industrial case studies.
Keywords:Inferential sensor  Real-time performance assessment  Bayesian inference  Reliability of predictions
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