Automating the Expansion of Instrument Typicals in Piping and Instrumentation Diagrams (P&IDs)
Paper i proceeding, 2025

Within the Engineering, Procurement, and Construction (EPC) industry, engineers manually create documents based on engineering drawings, which can be time-consuming and prone to human error. For example, the expansion of typical assemblies of instrument items (Instrument Typicals) in Piping and Instrumentation Diagrams (P&IDs) is a labor-intensive task. Each Instrument Typical assembly is depicted in the P&IDs via a simplified representation showing only a subset of the utilized instruments. The expansion activity involves recording all utilized instruments to create an instrument item list document based on the P&IDs for a particular EPC project. Fortunately, Artificial Intelligence (AI) could help automate this process. In this paper, we propose the first method for automating the process of Instrument Typical expansion in P&IDs. The method utilizes computer vision techniques and domain knowledge rules to extract information about the Instrument Typicals from a project's P&IDs and legend sheets. Subsequently, the extracted information is used to automatically generate the listing of all utilized instruments. The effectiveness of our method is evaluated on P&IDs from large industrial EPC projects, resulting in precision rates exceeding 98% and recall rates surpassing 99%. These results demonstrate the suitability of our method for industrial deployment. The successful application of our method has the potential to reduce engineering costs and increase the efficiency of EPC projects. Furthermore, the method could be adapted for additional applications in the EPC industry, which highlights the method's industrial value.

Författare

Vasil Shteriyanov

Technische Universiteit Eindhoven

McDermott

Rimman Dzhusupova

McDermott

Technische Universiteit Eindhoven

Jan Bosch

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Technische Universiteit Eindhoven

Helena Holmström Olsson

Malmö universitet

Proceedings of the AAAI Conference on Artificial Intelligence

21595399 (ISSN) 23743468 (eISSN)

Vol. 39 28 28885-28891
157735897X (ISBN)

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Philadelphia, USA,

Ämneskategorier (SSIF 2025)

Programvaruteknik

Datavetenskap (datalogi)

Den kondenserade materiens fysik

DOI

10.1609/aaai.v39i28.35155

Mer information

Senast uppdaterat

2025-05-09