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

Abstract

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.

New Tools for the Classification and Filtering of Historical Maps

“…Text/non-text separation for technical drawings and for maps presents some common characteristics, but for drawings the focus is on the detection of symbols, their localization and the identification of their connections [64] after image binarization, with the creation of specific tools [65].…”

Ontology-based computer aid for the automation of HAZOP studies

“…Another idea for future research approaches would be to use intelligent P&IDs ( Kang et al, 2019 ). This would reduce the effort to model the process plant using the process unit model library.…”

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

“…Major components of a diagram include symbols carrying field-specific meanings, lines representing inter-symbol connections, and attributes assigned to symbols and lines through texts or characters [1]. There have been studies on the recognition of diagrams such as electric diagram [2,3], engineering diagram [4][5][6], and logic diagram [7]. These research studies used traditional image recognition methods using geometric features of objects.…”

“…Some studies on recognizing P&IDs have also been conducted recently [4,6,12,13]. To recognize symbols, lines, and texts in P&IDs, these research studies also applied traditional image recognition methods.…”

“…Likewise, due to technological difficulties, the recognition of texts in P&IDs have also been conducted restrictively with rule-based approaches. Recently, more advanced research on recognizing complex P&IDs has been conducted using various feature-based methods and Tesseract OCR engine [6]. However, it is still difficult to overcome the inherent limitations of the traditional methods.…”