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

Symbols classification in engineering drawings.

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we present a semi-automatic and heuristic-based approach to detect and localise symbols within these drawings. This includes generating a labeled dataset from real world engineering drawings and investigating the classification performance of three different state-of the art supervised machine learning algorithms. In order to improve the classification accuracy the dataset was pre-processed using unsupervised learning algorithms to identify hidden patterns within classes. Testing and evaluating the proposed methods on a dataset of symbols representing one standard of drawings, namely Process and Instrumentation (P&ID) showed very competitive results

Smart P&IDs

Piping and instrumentation diagrams (P&IDs) are a universal language spoken in the process engineering industry. Intelligent piping and instrumentation diagrams have been created to ensure efficiency and organization in design processes through the incorporation of a back-end database of CAD drawings. We worked alongside DPS Engineering to incorporate intelligent P&IDs into the design of a small scale biodiesel transesterification process. To evaluate the potential application of intelligent P&IDs, we utilized the pilot unit operations laboratory in WPI’s Goddard Hall. Through professional opinion as well as from our experience, we determined that intelligent P&IDs are an efficient organization tool for large scale projects; however, for small processes, they are unnecessary

Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks.

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges or process diagrams, the three main shapes to be localised are the symbols, text and connectors. While most of the state of the art devotes to top-down recognition approaches which attempt to recognise these shapes based on their features and attributes, less work has been devoted to localising the actual pixels that constitute each shape, mostly because of the difficulty in obtaining a reliable source of training samples to classify each pixel individually. In this work, we present a convolutional neural network (CNN) capable of classifying each pixel, using a type of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID) as a case study. To obtain the training patches, we have used a semi-automated heuristics-based tool which is capable of accurately detecting and producing the symbol, text and connector layers of a particular P&ID standard in a considerable amount of time (given the need of human interaction). Experimental validation shows that the CNN is capable of obtaining these three layers in a reduced time, with the pixel window size used to generate the training samples having a strong influence on the recognition rate achieved for the different shapes. Furthermore, we compare the average run time that both the heuristics-tool and the CNN need in order to produce the three layers for a single diagram, indicating future directions to increase accuracy for the CNN without compromising the speed

Plant Information Modelling, Using Artificial Intelligence, for Process Hazard and Risk Analysis Study

In this research, the application of Artificial Intelligence and knowledge engineering, automation of equipment arrangement design, automation of piping and support design, using machine learning to automate the stress analysis, and finally, using information modelling to shift ‘field weld locating’ activity from the construction to the design phase were investigated. The results of integrating these methods on case studies, to increase the safety in the lifecycle of process plants were analysed and discussed

New trends on digitisation of complex engineering drawings

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions

Digital interpretation of sensor-equipment diagrams.

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

LabVIEW Improvements and Equipment Redesign of Unit Operations Distillation Column

Unit Operations labs are important tools to provide hands-on learning with various chemical processes. The distillation lab experiment allows students to connect equations and definitions from textbooks to the physical processes of distillation as they occur. LabVIEW was the software development tool used to create a program to control the distillation column from startup to shutdown. Improvements were made to both the distillation process equipment and the LabVIEW code to create a more interactive lab experience, enhance students’ conceptual understanding of distillation, and prepare students for their future career as chemical engineers. Equipment addressed included non-linear valves, malfunctioning temperature and differential pressure measurements, unnecessary piping in the system, and inaccessible equipment. The University of Louisville partnered with C&I Engineering in order to develop a redesign of the distillation column system to improve equipment accessibility. Upgrades to the LabVIEW code were made, including converting the overall architecture to a state machine, isolating the logic operations, and removing unnecessary code. Improvements made within the scope of this project have produced a safe yet adaptable foundation for the distillation lab and software. The proposed redesign will create a more streamlined process, better suited to enhance the student learning experience. The updated LabVIEW code is safer for operating the distillation column, allows for an increase in process control, and can be easily adapted for future projects to meet evolving learning objectives

Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes