Online soft sensor for hybrid systems with mixed continuous and discrete measurements

0367111 - ÚTIA 2012 RIV NL eng J - Journal Article
Suzdaleva, Evgenia - Nagy, Ivan
Online soft sensor for hybrid systems with mixed continuous and discrete measurements.
Computers and Chemical Engineering. Roč. 36, č. 10 (2012), s. 294-300. ISSN 0098-1354. E-ISSN 1873-4375
R&D Projects: GA MŠMT 1M0572; GA TA ČR TA01030123
Grant - others:Skoda Auto, a.s.(CZ) ENS/2009/UTIA
Institutional research plan: CEZ:AV0Z10750506
Keywords : online state prediction * hybrid filter * state-space model * mixed data
Subject RIV: BC - Control Systems Theory
Impact factor: 2.091, year: 2012
http://library.utia.cas.cz/separaty/2011/AS/suzdaleva-online soft sensor for hybrid systems with mixed continuous and discrete measurements.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0201889