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2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)-Proceedings Cover PDF
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kirchhübel Jørgensen Martini, Thomas |
| Copyright Year | 2019 |
| 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. Keywords—Bayesian methods, Expert systems, Process Control, Fault diagnosis, Fault-trees |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/etfa45715.2019 |
| Alternate Webpage(s) | https://backend.orbit.dtu.dk/ws/portalfiles/portal/192716579/ETFA_paper.pdf |
| Alternate Webpage(s) | https://backend.orbit.dtu.dk/ws/files/192716579/ETFA_paper.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/etfa45715.2019 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
