SYSTEM PLANOWANIA I KONTROLI PRODUKCJI Z WYKORZYSTANIEM CZUJNIKÓW TOMOGRAFICZNYCH

The article presents an idea of a production process control system. Advanced automation and control of production processes play a key role in maintaining competitiveness. The proposed solution consists of sensor networks for measurement process parameters, production resources and equipment state. The system uses wired and wireless communication, which gives possibility to acquisition data from existing in enterprise sensors and systems as well as acquisition data from new systems and sensors used to measure all processes, starting from production preparation to the final product. The solution contains process tomography sensors based on electrical capacitance tomography, electrical impedance tomography and ultrasound tomography. The use of tomographic methods enables to manage the intelligent structure of the companies in terms of processes and products. Industrial tomography enables observation of physical and chemical phenomena without the need to penetrate inside. It will enable the optimization and auto-optimization of design processes and production. Such solutions can operate autonomously, monitor and control measurements. All sensors return to the system continuous data about state of processes in some technologically closed objects like fermenters. Process tomography can also be used to acquisition data about a flow of liquids and loose ingredients in pipeline based on transport systems. Data acquired from sensors are collected in data warehouses in order to future processing and building the knowledge base. The results of the data analysis are showed in user control panels and are used directly in the control of the production process to increase the efficiency and quality of the products. Control methods cover issues related to the processing of data obtained from various sensors located at nodes. Monitoring takes place within the scope of acquired and processed data and parameter automation.W artykule przedstawiono ideę systemu kontroli procesu produkcyjnego. Zaawansowana automatyzacja i kontrola procesów produkcyjnych odgrywają kluczową rolę w utrzymaniu konkurencyjności. Proponowane rozwiązanie składa się z sieci czujników do pomiaru parametrów procesu, zasobów produkcyjnych i stanu wyposażenia. System wykorzystuje komunikację przewodową i bezprzewodową, która umożliwia pozyskiwanie danych z istniejących w przedsiębiorstwach czujników i systemów, a także pozyskiwanie danych z nowych systemów i czujników używanych do pomiaru wszystkich procesów, począwszy od przygotowania produkcji do produktu końcowego. Rozwiązanie zawiera czujniki tomografii procesowej oparte na elektrycznej tomografii pojemnościowej, elektrycznej tomografii impedancyjnej i tomografii ultradźwiękowej. Zastosowanie metod tomograficznych umożliwia zarządzanie inteligentną strukturą firm pod względem procesów i produktów. Tomografia przemysłowa umożliwia obserwację zjawisk fizycznych i chemicznych bez potrzeby penetracji wewnątrz. Umożliwi to optymalizację i automatyczną procesów projektowych i produkcji. Takie rozwiązania mogą działać autonomicznie, monitorować i kontrolować pomiary. Wszystkie czujniki przekazują do systemu ciągłe dane o stanie procesów w niektórych technologicznie zamkniętych obiektach, takich jak fermentory. Tomografia procesowa może być również wykorzystywana do pozyskiwania danych o przepływie płynów i luźnych składników w rurociągu w oparciu o systemy transportowe. Dane uzyskane z czujników gromadzone są w hurtowniach danych w celu ich dalszego przetwarzania i budowania bazy wiedzy. Wyniki analizy danych są wyświetlane w panelach sterowania użytkownika i są wykorzystywane bezpośrednio w kontroli procesu produkcyjnego w celu zwiększenia wydajności i jakości produktów. Metody kontroli obejmują zagadnienia związane z przetwarzaniem danych uzyskanych z różnych czujników zlokalizowanych w węzłach. Monitorowanie odbywa się w ramach pozyskanej i przetwarzanej automatyzacji danych i parametrów

KONCEPCJA SYSTEMU DETEKCJI DO LOKALIZACJI WEWNĄTRZ OBSZARÓW ZAMKNIĘTYCH Z WYKORZYSTANIEM TOMOGRAFII RADIOWEJ

System for navigation and localization inside buildings will determine man's location in confined spaces with level of accuracy never yet achieved. System's operation will be mainly based on Bluetooth signals in accordance with the Beacon standard. A worldwide innovation will be the use of Radio Tomographic Imaging (RTI). This technology will enable tracking of moving objects by image reconstruction based on changes of electromagnetic field signal strength (RSS).System do nawigacji wewnątrz budynków będzie umożliwiał określenie pozycji człowieka w zamkniętych pomieszczeniach z dużą precyzją. Działanie systemu będzie opierało się na sygnałach Bluetooth zgodnie ze standardem Beacon.. Innowacyjnym rozwiązaniem będzie zastosowanie tomografii radiowej (ang. Radio Tomographic Imaging (RTI)). Wykorzystanie tej technologii umożliwi śledzenie poruszających się obiektów poprzez rekonstrukcję obrazu na podstawie zmian siły sygnału (RSS) pola elektromagnetycznego

SENSOR PLATFORM OF INDUSTRIAL TOMOGRAPHY FOR DIAGNOSTICS AND CONTROL OF TECHNOLOGICAL PROCESSES

This article presents an industrial tomography platform used to diagnose and control technological processes. The system has been prepared so that it is possible to add individual sensors cooperating with the system of an intelligent cyber-physical platform with an open architecture. Additionally, it is possible to configure and cooperate with external systems freely. As part of the experimental work, a platform has been developed that allows individual subsystems and external customer systems to work together. The cyber-physical system, a new generation of digital systems, focuses mainly on the complex interplay and integration between cyberspace and the physical world. A cyber-physical system consists of highly integrated computational, communication, control and physical elements. The solution focuses mainly on the complex interplay and integration between cyberspace and the physical world

Industrial tomography platform for diagnostics and control of the crystallization process

PURPOSE: The aim of the article is an industrial system platform for diagnostics and control of the crystallization process with the use of tomographic technologies.DESIGN/METHODOLOGY/APPROACH: Various methods are used to study crystallization processes. Here, the tomographic method has been applied.FINDINGS: Tomography of industrial processes is a harmless, non-invasive imaging technique used in various industrial in-process technologies. It plays an important role in continuous data measurement for better understanding and monitoring of industrial processes, providing a fast and dynamic response that facilitates real-time process control, fault detection and system malfunctions.PRACTICAL IMPLICATIONS: Sensor networks with their feedback loops are fundamental elements of production control. A critical difference in the mass production of chemicals, metals, building materials, food and other commodities is that common process sensors provide only local measurements, e.g. temperature, pressure, fill level, flow rate or species concentration. However, in most production systems such local measurements are not representative of the entire process, so spatial solutions are required. Here the future belongs to distributed and image sensors.ORIGINALITY/VALUE: The concept of a system based on industrial tomography represents a solution currently unavailable on the world market, in its assumptions and effects it has a legitimate character of innovation on a global scale. At the same time, it means the creation of a new, fundamentally different from those available on the market, universal product in the technological sphere. It is an innovative, efficient tool for diagnostics and process control.peer-reviewe

Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection

Wet foundations and walls of buildings significantly increase the energy consumption of buildings, and the drying of walls is one of the priority activities as part of thermal modernization, along with the insulation of the facades. This article discusses the research findings of detecting moisture decomposition within building walls utilizing electrical impedance tomography (EIT) and deep learning techniques. In particular, the focus was on algorithmic models whose task is transforming voltage measurements into spatial EIT images. Two homogeneous deep learning networks were used: CNN (Convolutional Neural Network) and LSTM (Long-Short Term Memory). In addition, a new heterogeneous (hybrid) network was built with LSTM and CNN layers. Based on the reference reconstructions’ simulation data, three separate neural network algorithmic models: CNN, LSTM, and the hybrid model (CNN+LSTM), were trained. Then, based on popular measures such as mean square error or correlation coefficient, the quality of the models was assessed with the reference images. The obtained research results showed that hybrid deep neural networks have great potential for solving the tomographic inverse problem. Furthermore, it has been proven that the proper joining of CNN and LSTM layers can improve the effect of EIT reconstructions

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

Electrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high-resolution tomographic images is problematic because it involves solving the so-called ill-posed inverse problem. Noisy input data cause problems, too. Therefore, the use of appropriate hardware solutions to eliminate this phenomenon is necessary. An important cause of obtaining accurate tomographic images may also be the incorrect selection of algorithmic methods used to convert the measurements into the output images. In a dynamically changing environment of a tank reactor, selecting the optimal algorithmic method used to create a tomographic image becomes an optimization problem. This article presents the machine learning method’s original concept of intelligent selection depending on the reconstructed case. The long short-term memory network was used to classify the methods to choose one of the five homogenous methods—elastic net, linear regression with the least-squares learner, linear regression with support vector machine learner, support vector machine model, or artificial neural networks. In the presented research, tomographic images of selected measurement cases, reconstructed using five methods, were compared. Then, the selection methods’ accuracy was verified thanks to the long short-term memory network used as a classifier. The results proved that the new concept of long short-term memory classification ensures better tomographic reconstructions efficiency than imaging all measurement cases with single homogeneous methods

Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography - a hybrid approach

The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography

Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research

Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that it is possible to perform some therapy using a robot. This article is a synthesis review of the literature on research with the use of robots in the therapy of children with the diagnosis of early childhood autism. The review includes scientific journals from 2005–2021. Using descriptors: ASD (Autism Spectrum Disorders), Social robots, and Robot-based interventions, an analysis of available research in PubMed, Scopus and Web of Science was done. The results showed that a robot seems to be a great tool that encourages contact and involvement in joint activities. The review of the literature indicates the potential value of the use of robots in the therapy of people with autism as a facilitator in social contacts. Robot-Assisted Autism Therapy (RAAT) can encourage child to talk or do exercises. In the second aspect (prompting during a conversation), a robot encourages eye contact and suggests possible answers, e.g., during free conversation with a peer. In the third aspect (teaching, entertainment), the robot could play with autistic children in games supporting the development of joint attention. These types of games stimulate the development of motor skills and orientation in the body schema. In future work, a validation test would be desirable to check whether children with ASD are able to do the same with a real person by learning distrust and cheating the robot

Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control

The research presented here concerns the analysis and selection of logistic regression with wave preprocessing to solve the inverse problem in industrial tomography. The presented application includes a specialized device for tomographic measurements and dedicated algorithms for image reconstruction. The subject of the research was a model of a tank filled with tap water and specific inclusions. The research mainly targeted the study of developing and comparing models and methods for data reconstruction and analysis. The application allows choosing the appropriate method of image reconstruction, knowing the specifics of the solution. The novelty of the presented solution is the use of original machine learning algorithms to implement electrical impedance tomography. One of the features of the presented solution was the use of many individually trained subsystems, each of which produces a unique pixel of the final image. The methods were trained on data sets generated by computer simulation and based on actual laboratory measurements. Conductivity values for individual pixels are the result of the reconstruction of vector images within the tested object. By comparing the results of image reconstruction, the most efficient methods were identified

Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control

The research presented here concerns the analysis and selection of logistic regression with wave preprocessing to solve the inverse problem in industrial tomography. The presented application includes a specialized device for tomographic measurements and dedicated algorithms for image reconstruction. The subject of the research was a model of a tank filled with tap water and specific inclusions. The research mainly targeted the study of developing and comparing models and methods for data reconstruction and analysis. The application allows choosing the appropriate method of image reconstruction, knowing the specifics of the solution. The novelty of the presented solution is the use of original machine learning algorithms to implement electrical impedance tomography. One of the features of the presented solution was the use of many individually trained subsystems, each of which produces a unique pixel of the final image. The methods were trained on data sets generated by computer simulation and based on actual laboratory measurements. Conductivity values for individual pixels are the result of the reconstruction of vector images within the tested object. By comparing the results of image reconstruction, the most efficient methods were identified