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Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes

  • The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users’ actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice.

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Author:Galia WeidlORCiD, Anders L. Madsen, S. Israelson
URL:https://www.sciencedirect.com/science/article/abs/pii/S009813540500133X?via%3Dihub
DOI:https://doi.org/10.1016/j.compchemeng.2005.05.005
Parent Title (English):Computers & Chemical Engineering
Document Type:Article
Language:English
Year of Completion:2005
Release Date:2023/12/07
Tag:Bayes Networks
GND Keyword:Prozessüberwachung; Fehlererkennung; Prozesssteuerung
Volume:2005
Issue:29/9
First Page:1996
Last Page:2009
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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