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.
|Title of host publication||Proceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation|
|Number of pages||6|
|Publication status||Published - 2019|
|Event||24th IEEE International Conference on Emerging Technologies and Factory Automation - Paraninfo de la Universidad de Zaragoza, Zaragoza, Spain|
Duration: 10 Sep 2019 → 13 Sep 2019
|Conference||24th IEEE International Conference on Emerging Technologies and Factory Automation|
|Location||Paraninfo de la Universidad de Zaragoza|
|Period||10/09/2019 → 13/09/2019|
- Bayesian methods
- Expert systems
- Process Control
- Fault diagnosis
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.