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Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search.

Rica, Elena; Alvarez, Susana; Moreno-Garcia, Carlos Francisco; Serratosa, Francesc

Authors

Elena Rica

Susana Alvarez

Francesc Serratosa



Contributors

Adam Krzyzak
Editor

Ching Y. Suen
Editor

Andrea Torsello
Editor

Nicola Nobile
Editor

Abstract

Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final end the regeneration of the original computer-aided design (CAD) file, which can be better visualised and analysed through diverse computer applications. Most efforts in literature and commercial applications have focused on converting these sheets into CAD files in an automated way. Nonetheless, this needs to be a zero-error process; as the final CAD will always be verified by an engineer for integrity and inspection. In this paper, we present a method that, on the one hand, highlights which components in the CAD are most likely to have been incorrectly identified, and on the other hand, facilitates the engineer to search some groups of components in these huge assets. These techniques are based on graph embedding, computer neural networks and sub-graph matching.

Citation

RICA, E., ALVAREZ, S., MORENO-GARCIA, C.F. and SERRATOSA, F. 2022. Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. In Krzyzak, A., Suen, C.Y., Torsello, A. and Nobile, N. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2022 Joint International Association for Pattern Recognition (IAPR) international workshops on statistical techniques in pattern recognition, and structural and syntactic pattern recognition (S+SSPR 2022), 26-27 August 2022, Montréal, Canada. Lecture notes in computer science, 13813. Cham: Springer [online], pages 274-282. Available from: https://doi.org/10.1007/978-3-031-23028-8_28

Conference Name 2022 Joint International Association for Pattern Recognition (IAPR) international workshops on statistical techniques in pattern recognition, and structural and syntactic pattern recognition (S+SSPR 2022)
Conference Location Montréal, Canada
Start Date Aug 26, 2022
End Date Aug 27, 2022
Acceptance Date Feb 22, 2022
Online Publication Date Jan 1, 2023
Publication Date Dec 31, 2022
Deposit Date Jan 5, 2023
Publicly Available Date Jan 1, 2024
Publisher Springer
Pages 274-282
Series Title Lecture notes in computer science
Series Number 13813
Series ISSN 0302-9743 ; 1611-3349
Book Title Structural, syntactic, and statistical pattern recognition
ISBN 9783031230271
DOI https://doi.org/10.1007/978-3-031-23028-8_28
Keywords Piping and instrumentation diagrams; Node classification; Sub-graph matching; Automatic validation; Graph embedding
Public URL https://rgu-repository.worktribe.com/output/1848696

Files

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