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Adaptive Root Cause Analysis under uncertainties in industrial process operation

  • We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created automatically from the XML-structured data. The interconnection between XML-failure trees is handled as object oriented instances of Bayesian sub-networks within master-network covering the entire process and monitoring its overall condition, output quality and equipment effectiveness. We implement sequential on-line adaptivity of models' parameters to reflect changes in process operation. The system learning can be supervised by user feedback on the actual root cause. The general RCA methodology is applied to plate cutting in a hot rolling mill.

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Metadaten
Author:Galia WeidlORCiD, Gerhard Vollmar, Erik Dahlquist
URL:https://www.researchgate.net/publication/262562640_Adaptive_Root_Cause_Analysis_under_uncertainties_in_industrial_process_operation#fullTextFileContent
Parent Title (English):The Foundations of Computer Aided Process Operations (FOCAPO 2003)
Document Type:Conference Proceeding
Language:English
Year of Completion:2003
Release Date:2023/12/11
Tag:Adaptive; Adaptive Root Cause Analysis; Root Cause; uncertainties
GND Keyword:Prozesssteuerung; Prozessmodell
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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