Automatic Information Extraction from Piping and Instrumentation Diagrams

Abstract

One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models.To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data structure for capturing the structure of the piping schematics. We have also evaluated our proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained extremely promising results. To the best of our knowledge, this is the first system that performs end-to-end data extraction from P&ID diagrams.

Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

“…To date, several studies [33][34][35] have reported the recognition of various types of diagrams, such as electrical diagrams, engineering diagrams, logic diagrams, and piping and instrumentation diagrams (P&ID). With recent developments in deep learning algorithms, there has been active research on the application of CNN-based deep learning methods in the process of diagram recognition [36][37][38][39][40]. In [36] recognition of a simple logic diagram with general application targets was done.…”

“…In [36] recognition of a simple logic diagram with general application targets was done. In the work in [37], although P&ID was a recognition target, preprocessing of the diagrams was not considered, and the recognition of lines and tables was not investigated. In [38], a method for recognizing symbols, lines, and texts included in P&IDs was proposed.…”

Deep learning for symbols detection and classification in engineering drawings

“…A framework for extracting information from P&ID drawings was presented very recently in [47]. The authors used a two-step approach.…”

Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

“…Also, [5] convert 3D pulp&paper plant designs to graphs in order to perform graph matching to identify similar, and thus reusable designs. [31] has presented an approach for extracting information from P&ID sheets by using deep learning networks and low-level image processing techniques for capturing inlets, outlets and pipelines as a treelike data structure. [32] uses graph abstractions to identify differences between process designs, as captured in 3D CAD models, and the as-built version of the plant, as captured by laser scans.…”