Generating Diagnostic Bayesian Networks from Qualitative Causal Models

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Abstract

Safety and efficiency of modern industrial plants can be improved by providing operators with effective digital assistants to diagnose abnormal situations occurring in the plant. To make sense of a large number of alarms, root cause analysis can help pinpoint the origin of an abnormal situation. We investigate the translation of qualitative causal models into Bayesian belief networks (BBN) to utilize efficient tools for probability inference. The diagnosis result of a fault scenario of the Tennessee-Eastman-Process highlight the feasibility of the principle approach and the ongoing research aims to fully leverage the potential of BBN.
Original languageEnglish
Title of host publicationProceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation
Number of pages6
PublisherIEEE
Publication date2019
ISBN (Electronic)978-1-7281-0303-7
Publication statusPublished - 2019
Event24th IEEE International Conference on Emerging Technologies and Factory Automation - Paraninfo de la Universidad de Zaragoza, Zaragoza, Spain
Duration: 10 Sep 201913 Sep 2019
http://www.etfa2019.org/

Conference

Conference24th IEEE International Conference on Emerging Technologies and Factory Automation
LocationParaninfo de la Universidad de Zaragoza
CountrySpain
CityZaragoza
Period10/09/201913/09/2019
Internet address

Keywords

  • Bayesian methods
  • Expert systems
  • Process Control
  • Fault diagnosis
  • Fault-trees

Cite this

Kirchhübel, D., & Jørgensen, T. M. (2019). Generating Diagnostic Bayesian Networks from Qualitative Causal Models. In Proceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation IEEE.