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

Smart Solutions: Smart Grid Demokit

Treball desenvolupat dins el marc del programa 'European Project Semester'.The purpose of this report is to justify the design choices of the smart grid demo kit. Something had to be designed to make a smart grid clear for people who have little knowledge about smart grids. The product had to be appealing and clear for people to understand. And eventually should be usable, for example, on an information market. The first part of the research consisted of looking how to shape the whole system. How the 'tiles' had to look to be interactive for users and what they should feature. One part of this was doing research to get to know more about the already existing knowledge amount users. Another research investigated what appeals the most to the users. After this, a concept was created in compliance with the group and the client. The concept consists of hexagonal tiles, each with a different function: houses, solar panels, wind turbines, factories and energy storages. These tiles are all different parts of a smart grid. When combining these tiles, it can be made clear to users how smart grids work. The tiles are fabricated using a combination of 3D printing and laser cutting. The tiles have laser cut symbols on top of them to show what part of the smart grid they are. Digital LED strips are on top of the tiles to show the direction of the energy flow, and the colors indicate if the tile is producing or consuming power from the grid. The tiles are connected to each other by the so called “grid blocks”. These blocks make up the central power grid and are also lighting up by LED strips. Each tile is equipped with a microcontroller which controls the LED strips and makes it possible for the different tiles to “talk” with each other. Using this, the central tile knows which tiles are connected to the system. The central tile controls all tiles and runs the simulation of the smart grid. For further development of the project, it can be investigated how to control and adjust the system from an external system, for example by a tablet. The final product consists of five tiles connected by seven grid blocks which show how a smart grid works

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

Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

Graphics Recognition -- from Re-engineering to Retrieval

Invited talk. Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we discuss how the focus in document analysis, generally speaking, and in graphics recognition more specifically, has moved from re-engineering problems to indexing and information retrieval. After a review of ongoing work on these topics, we propose some challenges for the years to come

Learning Within Socio-Political Landscapes: (Re)imagining Children’s Geographies

Over a century ago, Lucy Sprague Mitchell, one of Bank Street College’s founders, put into practice a vision of teaching and learning enmeshed in the physical, social, and political city spaces of young peoples’ daily lives. Central to her work was reimagining geography, grounding the discipline in the here and now of children’s neighborhoods, connecting with community members and city spaces as a means to explore complex relationships within the wider world. Mitchell considered working across different modes of engagement as an integral practice for children to learn about their worlds and their roles within it: physical movement, like walking and subway riding, and the construction of maps with varying scales, materials, and symbols (Mitchell, 1991). Mitchell also envisioned movement and mapping as essential for teachers’ learning, leading multi-day Long Trips along the eastern seaboard to make visible educators’ connections to contemporary social, political, and environmental realities, and connecting city and rural locales. Temporally, these practices and tools acted as playful intermediaries between visible and invisible interrelationships constituting children’s and adult’s lives and livelihoods

ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.

The domain of materials for design is changing under the influence of an increased technological advancement, miniaturization and democratization. Materials are becoming connected, augmented, computational, interactive, active, responsive, and dynamic. These are ICS Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the world are experimenting with these new materials, there is the need to reflect on their potentials and impact on design. This paper is a first step in this direction: to interpret and describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge

Impacting Science Observation Skills Through Drawing Training

The research addressed was, how does drawing training impact the observational abilities of students? The list of motivating factors for this capstone includes students exhibiting a lack of ability to sit still and observe details of the world around them as well as exhibiting a lack of ability to design and communicate through drawings used in engineering. The author teaches art and science and has experienced these deficiencies. The author used an in-class project currently in the curriculum as the basis for the research. This project involved observing a reptile habitat as a subject for drawing. Betty Edwards’ book Drawing on the Right Side of the Brain (1999) was a key influence and resource for analyzing and supporting the impact drawing training has on observational skills in science class, but also in being a more complete observer of the world these students live