[pt] Com a chegada da Industria 4.0, empresas aderiram a usar gêmeos digitais para melhorar seus processos de produção e as condições de trabalhos de seus empregados. Os gêmeos digitais são normalmente associados a modelos tridimensionais, permitindo a realização de planejamentos, extração de dados, simulação e treinamento a partir das condições reais. Infelizmente, gêmeos digitais incorretos ou desatualizados podem induzir a erros e a desencontro de informações o que retira todas as vantagens do processo de virtualização, arruinando quaisquer comparativos com a realidade. Em contrapartida, gêmeos digitais ricos em informação permitem que simulações e extrações de dados sejam mais fieis a realidade. Atualmente, as tecnologias capazes de enriquecer as informações de gêmeos digitais são escassos, pois é um procedimento que leva tempo devido a necessidade de análises de especialistas, custos, equipamentos e ferramentas específicas. Recursos como fotografias 360 graus, vídeos e modelos tridimensionais podem ser usados para realizar uma avaliação e atualização nos gêmeos digitais. Porém, diferenças temporais, condições do ambiente e erros humanos entre os recursos podem gerar confusão durante a transferência e conexão da informação. Este trabalho apresenta uma ferramenta que explora as vantagens de combinar fotografias 360 graus com modelos 3D para gerar gêmeos digitais as-built. Cada imagem pode ser ajustada a uma localização dentro do sistema de coordenadas do modelo, inclusive permitindo alterações nos eixos e no campo de visão. Durante a navegação, é possível navegar livremente pelo modelo e pelas posições de interesse criadas pelo usuário. Além da visualização, a ferramenta propõe uma interação mais eficaz para realizar anotações entre modelos e fotografias 360 graus com o propósito de verificar consistências ou agregar novas informações ao gêmeo digital. Estas interações são importantes para a inspeção e manutenção, como avaliação de peças, análise das condições atuais ou a criação de comparativos entre o planejado e o real. / [en] With the arrival of Industry 4.0, companies have adopted digital twins to improve their production processes and the working conditions of their employees. Digital twins are generally associated with three-dimensional models and allow planning, data extraction, simulation, and training based on current conditions. Unfortunately, incorrect or outdated digital twins can lead to errors and information mismatch, which takes away all the advantages of the virtualization and computerization process, ruining any comparisons with reality. In contrast, information-rich digital twins allow simulations and data extraction to be more faithful to reality. Currently, technologies capable of enriching the information of digital twins are scarce, as it is a procedure that takes time due to the need for expert analysis, costs, equipment, and specific tools. Resources such as 360 degrees photographs, videos, and 3D models can be used to perform an evaluation and update the digital twins. However, temporal differences, environmental conditions, and human errors between the images and the model can generate confusion during the transfer and connection of information. This work presents a tool that explores the advantages of combining 360 degrees photographs with 3D models to generate as-built digital twins. Each image can be adjusted to a location within the model s coordinate system, allowing changes to axes and field of view. During navigation, it is possible to navigate the model and the user-created positions of interest freely. In addition to visualization, the tool proposes a more effective interaction to annotate between models and 360 degrees photographs to verify consistency or add new information to the digital twin. These interactions are essential for inspection and maintenance, such as evaluating parts, analyzing current conditions, or creating comparisons between planned and actual.

BACKGROUND - The development of Industry 4.0 increases the opportunities to both automate and digitize processes in the manufacturing industry. The steel industry has been around for many years, which means firmly anchored operations and both manual- and automated processes. To make better decisions, identify bottlenecks, and test new functions without having to stop the production, a digital model of the process can be helpful. Furthermore, with the rapid development of technology, digital models can be further developed into digital twins. A digital twin should be able to handle the communication between the physical- and digital world automatically and analyze data to make decisions in the process. RESEARCH QUESTIONS What are the challenges of developing a digital model representing a production line within a global steel manufacturing company? What opportunities could a digital model of a production line entail, and how could Industry 4.0 technologies create opportunities to further develop the digital model into a digital twin? METHODS - In this project, both a literature- and case study have been carried out. During the literature study, techniques that can be used to develop the digital model further have been investigated. During the case study, a digital model of a Quench Line was developed to gather practical experience of what it can mean to create a digital model of a manufacturing process within a steel manufacturing company. The model has been developed in MATLAB/Simulink. RESULTS - The most significant challenges when developing digital flow simulation models identified in this project were data management/access, handling variations, verifying the model, andlack of knowledge linked to digital models in general. The opportunities identified and confirmed in this project were that the model could be used to carry out new logistics planning, bottleneck analyses, and test new machine implementations. To further develop the digital model into a digital twin, Industry 4.0 technologies will be crucial. The technologies that will be useful are the Internet of Things, Artificial Intelligence, Machine Learning, Cloud Computing, and Big Data.

Green light optimal speed advisory (GLOSA) systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Deployment of successful infrastructure to vehicle communication requires Signal Phase and Timing (SPaT) messages to be populated with most likely estimates of switching times and confidence levels in these estimates. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This dissertation explores the different ways in which predictions can be made for the most likely switching times. Data are gathered from six intersections along the Gallows Road corridor in Northern Virginia. The application of long-short term memory neural networks for obtaining predictions is explored for one of the intersections. Different loss functions are tried for the purpose of prediction and a new loss function is devised. Mean absolute percentage error is found to be the best loss function in the short-term predictions. Mean squared error is the best for long-term predictions and the proposed loss function balances both well. The amount of historical data needed to make a single accurate prediction is assessed. The assessment concludes that the short-term prediction is accurate with only a 3 to 10 second time window in the past as long as the training dataset is large enough. Long term prediction, however, is better with a larger past time window. The robustness of LSTM models to different demand levels is then assessed utilizing the unique scenario created by the COVID-19 pandemic stay-at-home order. The study shows that the models are robust to the changing demands and while regularization does not really affect their robustness, L1 and L2 regularization can improve the overall prediction performance. An ensemble approach is used considering the use of transformers for SPaT prediction for the first time across the six intersections. Transformers are shown to outperform other models including LSTM. The ensemble provides a valuable metric to show the certainty level in each of the predictions through the level of consensus of the models. Finally, a hybrid approach integrating deep learning and controller logic is proposed by predicting actuations separately and using a digital twin to replicate SPaT information. The approach is proven to be the best approach with 58% less mean absolute error than other approaches. Overall, this dissertation provides a holistic methodology for predicting SPaT and the certainty level associated with it tailored to the existing technology and communication needs. / Doctor of Philosophy / Automated and connected vehicles waste a lot of fuel and energy to stop and go at traffic signals. The ideal case is for them to be able to know when the traffic signal turns green ahead of time and plan to reach the intersection by the time it is green, so they do not have to stop. Not having to stop can save up to 40 percent of the gas used at the intersection. This is a difficult task because the green time is not fixed. It has a minimum and maximum setting, and it keeps extending the green every time a new vehicle arrives. While this is good for adapting to traffic, it makes it difficult to know exactly when the traffic signal turns green to reach the intersection at that time. In this dissertation, different models to know ahead of time when the traffic signal will change are used. A model is chosen known as long-short term memory neural network (LSTM), which is a way to recognize how the traffic signal is expected to behave in the future from its past behavior. The point is to reduce the errors in the predictions. The first thing is to look at the loss function, which is how the model deals with error. It is found that the best thing is to take the average of the absolute value of the error as a percentage of the prediction if the prediction is that traffic signal will change soon. If it is a longer time until the traffic signal changes, the best way is to take the average of the square of the error. Finally, another function is introduced to balance between both. The second thing explored is how far back in time data was needed to be given to the model to predict accurately. For predictions of less than 20 seconds in the future, only 3 to 10 seconds in the past are needed. For predictions further in the future, looking further back can be useful. The third thing explored was how these models would do after rare events like COVID-19 pandemic. It was found that even though much fewer cars were passing through the intersections, the models still had low errors. Techniques were used to reduce the model reliance on specific data known as regularization techniques. This did not help the models to do better after COVID, but two techniques known as L1 and L2 regularization improved overall performance. The study was then expanded to include 6 intersections and used three additional models in addition to LSTM. One of these models, known as transformers, has never been used before for this problem and was shown to make better predictions than other models. The consensus between the models, which is how many of the models agree on the prediction, was used as a measure for certainty in the prediction. It was proven to be a good indicator. An approach is then introduced that combines the knowledge of the traffic signal controller logic with the powerful predictions of machine learning models. This is done by making a computer program that replicates the logic of the traffic signal controller known as a digital twin. Machine learning models are then used to predict vehicle arrivals. The program is then run using the predicted arrivals to provide a replication of the signal timing. This approach is found to be the best approach with 58 percent less error than the other approaches. Overall, this dissertation provides an end-to-end solution that uses real data generated from intersections to predict the time to green and estimate the certainty in prediction that can help automated and connected vehicles be more fuel efficient.

The Advanced Manufacturing section at ÅF Pöyry AB is exploring the possibilities of virtual commissioning and digital twins. As part of this exploration, this thesis sets out to demonstrate a method of developing scalable, reusable and realistic digital mechatronic models – the heart of a digital twin – for the virtual environment in RobotStudio. Research has shown that one of the major obstacles to implementing virtual commissioning as a standard in industry today is the lack of scalable and reusable digital twins. This is also the experience of ÅF Pöyry AB. After reviewing existing and proposed methods for developing digital twins, this thesis explains the necessary steps for developing a SmartComponent in RobotStudio, using the programming language C#. The results show that the SmartComponent developed is scalable and thus reusable. It works with grippers with any number of fingers and allows gripping by applying pressure to the target object from both the outside and the inside. It is also realistic in the sense that the interaction between the grippers and the objects to be picked in the virtual environment behaves and looks like it does in reality. The implementation of the SmartComponent developed is much faster and less complex than the method used today at ÅF Pöyry AB. The downsides of the developed method are the added competence required of the automation engineer and the risk that the digital twin may not be future-proof. / Avdelningen Advanced Manufacturing på ÅF Pöyry AB utforskar möjligheter med ”Virtual Commissioning” och ”Digital Twins”. Som ett led i detta projekt har syftet med detta examensarbete varit att visa ett sätt att utveckla en skalbar, återanvändbar och realistisk digital mekatronisk modell för den virtuella miljön i RobotStudio. Tidigare forskning har visat, i linje med ÅF Pöyry AB:s satsning, att ett av de stora hinder för att Virtual Commissioning ska kunna implementeras som standard i industrin idag är bristen av skalbara och återanvändbara digitala tvillingar. Efter en genomgång av den befintliga och föreslagna metoden för att utveckla digitala tvillingar presenteras de nödvändiga stegen för att utveckla en SmartComponent för RobotStudio, med programmeringsspråket C#. Resultaten av utvecklingen och testen har visat att den utvecklade SmartComponent är skalbar och återanvändbar: den fungerar med gripare oavsett antal fingrar och den tillåter gripning både genom att applicera tryck på plockobjektet från utsidan och insidan. Den är också realistiskt på så vis att interaktionen mellan griparen och objekten som ska plockas i den virtuella miljön beter sig och ser ut som i verkligheten. Implementeringen av den utvecklade SmartComponent är också mycket effektivare och mindre komplex jämfört med den metod som används idag på ÅF Pöyry AB. Nackdelarna med den föreslagna metoden är de extra kompetenskraven för automationsingenjörer och risken att den digitala tvillingen inte är framtidssäker.

This thesis presents the design and prototype implementation of a digital twin based on a knowledge graph for Internet of Things (IoT) platforms. The digital twin is a virtual representation of a physical object or system that must continually integrate and update knowledge in rapidly changing environments. The proposed knowledge graph is designed to store and efficiently query a large number of IoT devices in a complex logical structure, use rule-based reasoning to infer new facts, and integrate unanticipated devices into the existing logical structure in order to adapt to changing environments. The digital twin is implemented using the open-source TypeDB knowledge graph and tested in a simplified automobile production line environment. The main focus of the work is on the integration of unanticipated devices, for which a similarity metric is implemented to identify similar existing devices and determine the appropriate integration into the knowledge graph. The proposed digital twin knowledge graph is a promising solution for managing and integrating knowledge in rapidly changing IoT environments, providing valuable insights and support for decision-making. / I den här avhandlingen presenteras utformningen och prototypimplementeringen av en digital tvilling baserad på en kunskapsgraf för IoT-plattformar (Internet of Things). Den digitala tvillingen är en virtuell representation av ett fysiskt objekt eller system som måste integrera och uppdatera kunskap i snabbt föränderliga miljöer. Den föreslagna kunskapsgrafen är utformad för att lagra och effektivt söka efter en stor uppsättning IoT-enheter i en komplex logisk struktur, använda regelbaserade resonemang för att härleda nya fakta och integrera oväntade enheter i den befintliga logiska strukturen för att anpassa sig till föränderliga miljöer. Den digitala tvillingen genomförs med hjälp av kunskapsgrafen TypeDB med öppen källkod och testas i en förenklad miljö för bilproduktion. Huvudfokus ligger på integrationen av oväntade enheter, för vilka ett likhetsmått implementeras för att identifiera liknande befintliga enheter och bestämma lämplig integration i kunskapsgrafen. Den föreslagna kunskapsgrafen för digitala tvillingar är en lovande lösning för att hantera och integrera kunskap i snabbt föränderliga IoT-miljöer, vilket ger värdefulla insikter och stöd för beslutsfattande. / Esta tesis presenta el diseño e implementación de un prototipo de gemelo digital basado en un grafo de conocimiento para plataformas de Internet de las Cosas (IoT). El gemelo digital es una representación virtual de un objeto o sistema físico que debe integrar y actualizar continuamente el conocimiento en entornos que cambian rápidamente. El grafo de conocimiento propuesto está diseñado para almacenar y consultar eficientemente un gran número de dispositivos IoT en una estructura lógica compleja, utilizar el razonamiento basado en reglas para inferir nuevos hechos e integrar dispositivos imprevistos en la estructura lógica existente para adaptarse a los cambios del entorno. El gemelo digital se implementa utilizando el grafo de conocimiento de código abierto TypeDB y se prueba en un entorno simplificado basado en una línea de producción de automóviles. El objetivo principal del trabajo es la integración de dispositivos no previstos, para lo cual se implementa una métrica de similitud para identificar dispositivos existentes similares y determinar la integración adecuada en el grafo de conocimiento. El grafo de conocimiento propuesto es una solución prometedora para la gestión del conocimiento y la integración en entornos IoT que cambian rápidamente, proporcionando información valiosa y apoyo a la toma de decisiones.

In order to succeed with the Sustainable Development Goals, the mining of minerals will need to not only continue, but increase in the coming years. The extraction of minerals such as gold and cobalt is often associated with human rights violations and the financing of criminal enterprises. This has led to increased pressure on firms to implement effective responsible sourcing practices. While often seen as a major challenge to many firms, if leveraged correctly, responsible sourcing can be turned into significant competitive advantages and increased supply chain visibility. Given the need for more research in this area, the aim of this study is to understand what responsible sourcing practices are used among manufacturing companies so that an approach for responsible sourcing of minerals can be developed. Using a qualitative research design and following a systematic combining approach, interviews with large industrial manufacturers were conducted. Using thematic analysis, this thesis identifies the main drivers, enablers, barriers, and strategies used by manufacturing companies. While the findings show that responsible sourcing is on the agenda for all companies, it is clear that some firms take responsible sourcing more seriously than others. Therefore, it is suggested that firms need to depart from a cost-centric perspective and instead see the benefits and long-term value generated from responsible sourcing. Furthermore, the findings suggest that some companies are becoming increasingly aware of the need to know their supply chain, and how responsible sourcing practices can be beneficial by providing supply chain visibility. However, the current practices are limited because they are an inherently static representation of a dynamic system. The implementation of a supply chain digital twin is therefore presented as a potential way for firms to improve their responsible sourcing practices while at the same time gaining substantial business value. / För att lyckas med målen för hållbar utveckling måste brytningen av mineraler inte bara fortsätta, utan även öka under de kommande åren. Utvinning av mineraler som guld och kobolt är ofta förknippad med kränkningar av mänskliga rättigheter och finansiering av kriminella organisationer. Detta har lett till ett ökat tryck på företag att införa effektiva metoder för ansvarsfullt inköp. Även om det ofta ses som en stor utmaning för många företag kan ansvarsfullt inköp, om det används på rätt sätt, omvandlas till betydande konkurrensfördelar och bidra till ökad synlighet i leveranskedjan. Med tanke på behovet av mer forskning på detta område är syftet med den här studien att förstå vilka metoder för ansvarsfullt inköp som används bland tillverkningsföretag, så att ett tillvägagångssätt för ansvarsfullt inköp av mineraler kan utvecklas. Genom en kvalitativ forskningsdesign och enligt en systematisk kombinationsmetod genomfördes intervjuer med stora tillverkande företag. Med hjälp av tematisk analys identifieras i denna avhandling de viktigaste drivkrafterna, möjliggörarna, hindren och strategierna som används av tillverkningsföretag. Resultaten visar att ansvarsfullt inköp står på agendan för alla företag, men det är tydligt att vissa företag tar ansvarsfullt inköp på större allvar än andra. Därför föreslås det att företag måste avvika från ett kostnadsfokuserat perspektiv och istället se fördelarna och det långsiktiga värdet som genereras av ansvarsfullt inköp. Dessutom tyder resultaten på att vissa företag blir allt mer medvetna om behovet av att känna till sin leverantörskedja och hur ansvarsfulla inköpsmetoder kan vara fördelaktiga genom att ge större synlighet i leverantörskedjan. De nuvarande metoderna är dock begränsade eftersom de är en statisk representation av ett dynamiskt system. Implementeringen av en digital tvilling i försörjningskedjan presenteras därför som ett potentiellt sätt för företag att förbättra sina metoder för ansvarsfulla inköp samtidigt som de får ett betydande affärsvärde.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.

Testování multikomponentních IT a IoT systémů, které zpracovávají proudy různých zpráv je složitou úlohou. Proč je to složité? Kvůli množství komponentů, jejích asynchronní interakcí, různým kombinacím události pro testování, testovacímu prostředí, které se liší od reálného a množství dalším důvodů. Táhle práce navrhuje způsob generování komplexních dat pro testovací účely s minimálním zásahem vývojářů. Generování dat založeno na analýze sledu komunikace reálného systému a následnou syntézou podobných sledů pro testování. Práce také navrhuje framework na prvotní analýzu zpráv přenášených v zachycené komunikace. Tohle může být uděláno použitím různých abstraktních modelů: modelu zprávy a modelu komunikaci. Výsledkem téhle práce je implementovaní knihovna na tvorbu modelu zprávy a množina operací pro práci s tímto modelem.