| Title | Towards Automated Generation of Function Models from P IDs |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Song, M., Lind, M. |
| Conference Name | 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) |
| Date Published | sep |
| Keywords | automated generation, Automated model generation, automationml, composability, Decision support systems, function model, Functional modeling, IDS, knowledge acquisition, MFM, MFM models, modeling patterns, modeling process, operator decision support systems, P&ids, plant topology models, production engineering computing, pubcrawl, resilience, Resiliency |
| Abstract | Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed. |
| DOI | 10.1109/ETFA46521.2020.9212146 |
| Citation Key | song_towards_2020 |
Towards Automated Generation of Function Models from P IDs