Digital interpretation of sensor-equipment diagrams.

Abstract

A sensor-equipment diagram is a type of engineering drawing used in the industrial practice that depicts the interconnectivity between a group of sensors and a portion of an Oil & Gas facility. The interpretation of these documents is not a straightforward task even for human experts. Some of the most common limitations are the large size of the drawing, a lack of standard in defining equipment symbols, and a complex and entangled representation of the connectors. This paper presents a system that, given a sensor-equipment diagram and a few impositions by the user, outputs a list with the reading of the content of the sensors and the equipment parts plus their interconnectivity. This work has been developed using open source Python modules and code, and its main purpose is to provide a tool which can help in the collection of labelled samples for a more robust artificial intelligence based solution in the near future.

Citation

MORENO-GARCÍA, C.F. 2018. Digital interpretation of sensor-equipment diagrams. In Martin, K., Wiratunga, N. and Smith, L.S. (eds.) Proceedings of the 2018 Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018), 27 June 2018, Aberdeen, UK. CEUR workshop proceedings, 2151. Aachen: CEUR Workshop ProceedingsCEUR-WS [online], session 2, paper 1. Available from: http://ceur-ws.org/Vol-2151/Paper_s2.pdf

• Conference Name: 2018 Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018)
• Conference Location: Aberdeen, UK
• Start Date: Jun 27, 2018
• Acceptance Date: Jun 8, 2018
• Online Publication Date: Jun 27, 2018
• Publication Date: Jul 30, 2018
• Deposit Date: Aug 31, 2018
• Publicly Available Date: Aug 31, 2018
• Print ISSN: 1613-0073
• Publisher: CEUR Workshop Proceedings
• Series Title: CEUR workshop proceedings
• Series Number: 2151
• Series ISSN: 1613-0073
• Keywords: Engineering drawings; Digitisation; Circle hough transform; Text graphics segmentation; Optical character recognition
• Public URL: http://hdl.handle.net/10059/3098
• Publisher URL: http://ceur-ws.org/Vol-2151/Paper_s2.pdf