Paper Info
Title
Generating an industrial process graph from 3D pipe routing information
Abstract
The automatic generation of digital twins of industrial processes requires the integration of several sources of information. If the twin is expected to accurately capture thermo-hydraulic phenomena, dimensions of tanks and other process components as well as detailed pipe routing information is relevant. Such information is not comprehensively captured in P&IDs (Piping & Instrumentation Diagrams), but it is available from 3D CAD models. However, information about control loops is not available from 3D CAD models, but is available from P&IDs. Previous research has demonstrated the extraction of such information from machine-readable P&IDs and 3D CAD models and converting this information to graphs. Further research is expected on applying graph matching methods for integrating these separate graphs to a common graph-based data structure that captures all of the desired information. This common model could support further work to develop digital twins. A major obstacle to this is that the graphs that have currently been generated from P&IDs and 3D CAD models are at very different abstraction levels, so graph matching methods are not feasible. This article address this obstacle by building on previous work, in which graphs were generated from P&IDs and 3D CAD models. The contribution of this paper is several novel algorithms for preprocessing a 3D CAD generated graph, until it is at the same level of abstraction as a P&ID generated graph of the same industrial process. The algorithms are demonstrated in the context of a laboratory process.
Year
DOI
Venue
2020
10.1109/ETFA46521.2020.9212175
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Keywords
DocType
Volume
industry 4.0,process industry,digitisation,automation,modelling and simulation,digital twins,3D modeling,3D CAD,digital plant,plant design
Conference
1
ISSN
ISBN
Citations 
1946-0740
978-1-7281-8956-7
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Seppo Sierla100.34
Mohammad Azangoo200.68
Valeriy Vyatkin31047152.80