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Description
  • 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.
  • 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. (en)
Title
  • Online soft sensor for hybrid systems with mixed contimuous and discrete measurements
  • Online soft sensor for hybrid systems with mixed contimuous and discrete measurements (en)
skos:prefLabel
  • Online soft sensor for hybrid systems with mixed contimuous and discrete measurements
  • Online soft sensor for hybrid systems with mixed contimuous and discrete measurements (en)
skos:notation
  • RIV/68407700:21260/12:00196517!RIV13-MSM-21260___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(TA01030123), S, Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika
  • 36
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 156573
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21260/12:00196517
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • online state prediction; hybrid filter; state-space model; mixed data (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [C88C6AF8F52A]
http://linked.open...i/riv/nazevZdroje
  • Computers and Chemical Engineering
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 10
http://linked.open...iv/tvurceVysledku
  • Nagy, Ivan
  • Suzdaleva, E.
http://linked.open...ain/vavai/riv/wos
  • 000298548800025
http://linked.open...n/vavai/riv/zamer
issn
  • 0098-1354
number of pages
http://bibframe.org/vocab/doi
  • 10.1016/j.compchemeng.2011.09.004
http://localhost/t...ganizacniJednotka
  • 21260
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