A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams.

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

Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection.

Construction drawings are frequently stored in undigitised formats and consequently, their analysis requires substantial manual effort. This is true for many crucial tasks, including material takeoff where the purpose is to obtain a list of the equipment and respective amounts required for a project. Engineering drawing digitisation has recently attracted increased attention, however construction drawings have received considerably less interest compared to other types. To address these issues, this paper presents a novel framework for the automatic processing of construction drawings. Extensive experiments were performed using two state-of-the-art deep learning models for object detection in challenging high-resolution drawings sourced from industry. The results show a significant reduction in the time required for drawing analysis. Promising performance was achieved for symbol detection across various classes, with a mean average precision of 79% for the YOLO-based method and 83% for the Faster R-CNN-based method. This framework enables the digital transformation of construction drawings, improving tasks such as material takeoff and many others

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.11Ysciescopu

Using artificial intelligence to find design errors in the engineering drawings

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project\u27s overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry

Reducing human effort in engineering drawing validation.

Oil & Gas facilities are extremely huge and have complex industrial structures that are documented using thousands of printed sheets. During the last years, it has been a tendency to migrate these paper sheets towards a digital environment, with the final end of regenerating the original computer-aided design (CAD) projects which are useful to visualise and analyse these facilities through diverse computer applications. Usually, this was done manually by re-sketching each page using CAD applications. Nevertheless, some applications have appeared which generate the CAD document automatically given the paper sheets. In this last case, the final document is always verified by an engineer due to the need of being a zero-error process. Since the need of an engineer is absolutely accepted, we present a new method to reduce the required engineer working time. This is done by highlighting the digitised components in the CAD document that the automatic method could have incorrectly identified. Thus, the engineer is required only to look at these components. The experimental section shows our method achieves a reduction of approximately 40% of the human effort keeping a zero-error process

Geometric and Computational Aspects of Manipulation Rules for Graph-Based Engineering Diagrams

The digitization of graph-based engineering diagrams like P&IDs or circuit drawings from optical sources as well as their subsequent processing involves both image understanding and semantic technologies. More precisely, after a raw graph has been obtained by an object detection and line extraction pipeline, semantic gaps (like resolving material flow directions) need to be overcome to retain a comprehensive, semantically correct graph. Likewise, the graph representation often needs to be altered to achieve interoperability with established CAE systems and to accommodate customer-specific requirements. Semantic technologies provide powerful tools to manipulate such data but usually require rather complicated implementation. Graphically presentable graph based rules provide a code-free mean to ease the interaction with domain experts. In order to be applicable in real-world applications, both geometric and computational aspects need to be considered. This paper explores these aspects and demonstrates use cases of such rule graphs

Sitting on a gold mine: the story of the process industry's automatic formation of a digital twin

The use of a software tool chain to generate Digital Twins (DTs) automatically can speed up digitization and lower development costs. Engineering documents and system data are just two examples of source information that can be used to generate a DT. After proposing a general plan for semi-automatic generation of a DT for a process system, this work describe our efforts to extract necessary information for the generation of a DT of a process system from existing information in a factory floor like piping and instrumentation diagrams (P&IDs). To extract initial raw model data, techniques such as image, pattern, and text recognition can be used, and then an intermediate graph model can be generated and modified based on requirements. In order to increase the system's adaptability and reliability, this research will delve deeper into the steps involved in creating and manipulating an intermediate graph model