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Causal structure learning in process engineering using Bayes Nets and soft interventions

 
: Kühnert, Christian; Bernard, Thomas; Frey, Christian

:
Postprint urn:nbn:de:0011-n-1859405 (652 KByte PDF)
MD5 Fingerprint: 40b00f44554b6877e58afc3a9a6a59a1
© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 30.8.2012


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society:
IEEE 9th International Conference on Industrial Informatics, INDIN 2011. Proceedings. Vol.1 : Caparica, Lisbon, Portugal, 26-29 July 2011
New York, NY: IEEE, 2011
ISBN: 978-1-4577-0433-8
ISBN: 978-1-4577-0435-2
ISBN: 1-4577-0435-8
pp.69-74
International Conference on Industrial Informatics (INDIN) <9, 2011, Caparica>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
As modern industrial processes become more and more complex, machine learning is increasingly used to gain additional knowledge about the process from production data. Up to now these methods are usually based on correlations between process parameters and hence cannot describe cause-effect relationships. To overcome this problem we propose a hybrid (constraint and scoring-based) structure learning method based on a Bayesian Network to detect the causal structure out of its data. Starting with an empty graph, first the underlying undirected graph is learned and edges coming from root nodes are directed using a constraint-based approach. Next a scoring-based method is used in order to calculate the uncertainty for each possible directed edge and out of this construct the Bayesian Network. Finally soft interventions are applied to the process in order to learn the real causal structure. Our approach is tested in simulations on a chemical stirred tank reactor and on an experimental laboratory plant.

: http://publica.fraunhofer.de/documents/N-185940.html