Reducing human effort in engineering drawing validation

Rica, Elena; Moreno‐García, Carlos Francisco; Álvarez, Susana; Serratosa, Francesc

doi:10.1016/j.compind.2020.103198

Cited by 23 publications

(8 citation statements)

References 28 publications

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“…This makes a universal approach to the digitization of existing P&ID of BES into a computer-interpretable form difficult. The approaches for the automatic processing of P&ID shown so far concentrate on the automatic analysis of industrial plants [15,[43][44][45][46][47][48][49][50][51][52][53]. Partly, the same technical systems are considered in the cross-sectional technology as in buildings.…”

Section: Related Workmentioning

confidence: 99%

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

Stinner,

Wiecek,

Baranski

et al. 2021

Preprint

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Buildings directly and indirectly emit a large share of current CO2 emissions. There is a high potential for CO2 savings through modern control methods in building automation systems (BAS) like model predictive control (MPC). For a proper control, MPC needs mathematical models to predict the future behavior of the controlled system. For this purpose, digital twins of the building can be used. However, with current methods in existing buildings, a digital twin set up is usually laborintensive. Especially connecting the different components of the technical system to an overall digital twin of the building is time-consuming. Piping and instrument diagrams (P&ID) can provide the needed information, but it is necessary to extract the information and provide it in a standardized format to process it further. In this work, we present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way. There are various standards for graphical representation of symbols in P&ID of building energy systems. Therefore, we use different data sources and standards to generate a holistic training data set. We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets. Furthermore, the result is exported to a format that provides semantics of building energy systems. The symbol recognition, line recognition and connection recognition show good results with an average precision of 93.7%, which can be used in further processes like control generation, (distributed) model predictive control or fault detection. Nevertheless, the approach needs further research.

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Section: Related Workmentioning

confidence: 99%

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

Stinner,

Wiecek,

Baranski

et al. 2021

Preprint

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show abstract

“…Therefore, we must resort to consider heuristic and automatic solutions to properly locate the text strings and symbols which depict pipe specs and connection points respectively. On the other hand, by correctly identifying these pointers, we are able to allow the user to manually mark up the corrosion sections, bringing us one step closer to identifying the structures depicted within the engineering diagram [12].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Toral

Moreno‐García

Elyan

et al. 2021

Lecture Notes in Computer Science

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Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest.

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“…The goal of this interaction is to allow effective operation of the machine from the human end, while the machine simultaneously feeds back information that aids the human to make decisions. Human interaction has been recently applied to image alignment for robotics pose and image alignment estimation [33,34], 2D-camera calibration [35] or engineering drawing validation [36].…”

Section: Interactive Machine Learningmentioning

confidence: 99%

Interactive online learning for graph matching using active strategies

Conte

Serratosa

2020

Knowledge-Based Systems

Self Cite

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In some pattern recognition applications, objects are represented by attributed graphs, in which nodes represent local parts of the objects and edges represent relationships between these local parts. In this framework, the comparison between objects is performed through the distance between attributed graphs. Usually, this distance is a linear equation defined by some cost functions on the nodes and on the edges of both attributed graphs. In this paper, we present an online, active and interactive method for learning these cost functions, which works as follows. Graphs are provided to the learning algorithm by pairs in a sequential order (online). Then, a correspondence between them is computed,

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Reducing human effort in engineering drawing validation

Cited by 23 publications

References 28 publications

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Interactive online learning for graph matching using active strategies

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