0367111 20240111140803.720111125235959.9 000298548800025 10.1016/j.compchemeng.2011.09.004 7 s. cav_un_epca*02564460098-13543610 (2012)294-300Elsevier online state prediction hybrid filter state-space model mixed data cav_un_auth*0108105 Suzdaleva Evgenia Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2011/AS/suzdaleva-online soft sensor for hybrid systems with mixed continuous and discrete measurements.pdf 1M0572 GA MŠk cav_un_auth*0001814 TA01030123 GA TA ČR cav_un_auth*0271776 ENS/2009/UTIA Skoda Auto, a.s. CZ CEZ:AV0Z10750506 Online state prediction and fault detection are typical tasks in the chemical industry. In practice it often happens that some variables, important and critical for quality control, cannot be measured online due to such restrictions as cost and reliability. An uncertainty existing in real systems allows to use a probabilistic approach to online state estimation. Such an approach is proposed in this paper. Different types of information appearing in an online diagnostic system are processed via combination of algorithms subject to probability distributions. This combination of algorithms is presented as a decomposed version of Bayesian filtering. In this paper, the proposed solution is specialized for a system with mixed both continuous and discrete-valued measurements and unobserved variables. 2012 BC 2 http://hdl.handle.net/11104/0201889 COMPUTERSCIENCEINTERDISCIPLINARYAPPLICATIONS|ENGINEERINGCHEMICAL 2.367 0.373 8.7 0.01103 0.63 7048 225 79 1.138 33.87 2.95 Q1 53.045 77.408 Q2 Q2 2012 000298548800025 WOS pdf cav_un_epca*0256446 Computers and Chemical Engineering 0098-1354 1873-4375 Roč. 36 č. 10 2012 294 300 Elsevier