Semantic Integration in the Context of Cyber-Physical Systems

Industrial systems have been developing into more and more complex systems during last decades. They have changed from centralized solutions to distributed, more robust, and more exible eco-systems comprising a high number of embedded systems. In recent years, we are witnessing the research trend in the area of embedded systems which concerns the very close integration of physical and computing systems. This dissertation thesis deals with the problem of the semantic integration of components (sensors and actuators) of cyber-physical systems within industrial automation domain and presents resulting bene ts. Cyber-physical systems were created based on the aforementioned trend of the close integration of computing systems and physical systems. This tight integration involves infrastructures responsible for control, computation, communication, and sensing. These systems are composed of many subsystems produced by various manufacturers, and the subsystems produce an enormous volume of data. Furthermore, data generated from all of the system parts has di erent dimensions, sampling rates, levels of details, etc. Next, cyber-physical systems form systems which represent building blocks of the fourth industrial revolution (Industry 4.0) for example (Industrial) Internet of Things, Smart Cities, Smart Factories. Thus, the right understanding of data (data meanings, given context, subsystems purposes, and possible ways of subsystems integration) belong to essential requirements for enabling Industry 4.0 visions. In this thesis, the utilization of ontologies was proposed to deal with the semantic heterogeneity for enabling easier cyber-physical system components integration. Moreover, the current widespread e ort to create exible highly customized manufacturing requires novel methods for data handling together with subsequent data utilization. Storing knowledge and data in an ontology o ers a needed solution. For example, an ontology employment brings easy system data model management, increase an e ciency of cyber-physical system components interoperability, advanced data processing, reusability of sensors and actuators, and utilization of ontology matching methods for an integration of other data models. This work concerns the problem, how to describe cyber-physical system components using ontologies to enable e ective integration. Next, the ontology matching system suitable for integration of heterogeneous data models in industrial automation domain is described. The proposed solution of the semantic interoperability is demonstrated on the Plug&Play cyber-physical system components. On the other hand, storing data in an ontology and mainly processing of RDF statements brings one signi cant bottleneck | performance issue. Thus, Big Data technologies are employed for overcoming this issue together with a proposal of suitable storage data models. The overall approach is demonstrated on the proposed and developed prototype named Semantic Big Data Historian. In particular, the main contributions of the dissertation thesis are as follows: 1. The proposal of the solution for CPS low-level semantic integration based on Semantic web Technologies together with a veri cation of a feasibility of proposed approach using Semantic Big Data Historian. 2. The overcoming performance issues of processing shop floor data represented as RDF-triples with the help of Big Data technologies and suitable storage data models | vertical partitioning and hybrid SBDH model. 3. The proposal and implementation of a suitable way how to integrate heterogeneous data models from industrial automation domain where the highest precision and recall are required. The approach is based on similarity measures aggregation using self-organizing maps and user involvement with the help of active learning and visualization of self-organizing map output layer. 4. Enabling reusability of cyber-physical system components together with effortless configuration based on utilization of Semantic Web technologies. This approach was named as Plug&Play cyber-physical system components.Katedra kybernetik

Knowledge-driven Artificial Intelligence in Steelmaking: Towards Industry 4.0

With the ongoing emergence of the Fourth Industrial Revolution, often referred to as Indus-try 4.0, new innovations, concepts, and standards are reshaping manufacturing processes and production, leading to intelligent cyber-physical systems and smart factories. Steel production is one important manufacturing process that is undergoing this digital transfor-mation. Realising this vision in steel production comes with unique challenges, including the seamless interoperability between diverse and complex systems, the uniformity of het-erogeneous data, and a need for standardised human-to-machine and machine-to-machine communication protocols. To address these challenges, international standards have been developed, and new technologies have been introduced and studied in both industry and academia. However, due to the vast quantity, scale, and heterogeneous nature of industrial data and systems, achieving interoperability among components within the context of Industry 4.0 remains a challenge, requiring the need for formal knowledge representation capabilities to enhance the understanding of data and information. In response, semantic-based technologies have been proposed as a method to capture knowledge from data and resolve incompatibility conflicts within Industry 4.0 scenarios. We propose utilising fundamental Semantic Web concepts, such as ontologies and knowledge graphs, specifically to enhance semantic interoperability, improve data integration, and standardise data across heterogeneous systems within the context of steelmaking. Addition-ally, we investigate ongoing trends that involve the integration of Machine Learning (ML)techniques with semantic technologies, resulting in the creation of hybrid models. These models capitalise on the strengths derived from the intersection of these two AI approaches.Furthermore, we explore the need for continuous reasoning over data streams, presenting preliminary research that combines ML and semantic technologies in the context of data streams. In this thesis, we make four main contributions: (1) We discover that a clear under-standing of semantic-based asset administration shells, an international standard within the RAMI 4.0 model, was lacking, and provide an extensive survey on semantic-based implementations of asset administration shells. We focus on literature that utilises semantic technologies to enhance the representation, integration, and exchange of information in an industrial setting. (2) The creation of an ontology, a semantic knowledge base, which specifically captures the cold rolling processes in steelmaking. We demonstrate use cases that leverage these semantic methodologies with real-world industrial data for data access, data integration, data querying, and condition-based maintenance purposes. (3) A frame-work demonstrating one approach for integrating machine learning models with semantic technologies to aid decision-making in the domain of steelmaking. We showcase a novel approach of applying random forest classification using rule-based reasoning, incorporating both meta-data and external domain expert knowledge into the model, resulting in improved knowledge-guided assistance for the human-in-the-loop during steelmaking processes. (4) The groundwork for a continuous data stream reasoning framework, where both domain expert knowledge and random forest classification can be dynamically applied to data streams on the fly. This approach opens up possibilities for real-time condition-based monitoring and real-time decision support for predictive maintenance applications. We demonstrate the adaptability of the framework in the context of dynamic steel production processes. Our contributions have been validated on both real-world data sets with peer-reviewed conferences and journals, as well as through collaboration with domain experts from our industrial partners at Tata Steel

Kommunikation und Bildverarbeitung in der Automation

In diesem Open Access-Tagungsband sind die besten Beiträge des 11. Jahreskolloquiums "Kommunikation in der Automation" (KommA 2020) und des 7. Jahreskolloquiums "Bildverarbeitung in der Automation" (BVAu 2020) enthalten. Die Kolloquien fanden am 28. und 29. Oktober 2020 statt und wurden erstmalig als digitale Webveranstaltung auf dem Innovation Campus Lemgo organisiert. Die vorgestellten neuesten Forschungsergebnisse auf den Gebieten der industriellen Kommunikationstechnik und Bildverarbeitung erweitern den aktuellen Stand der Forschung und Technik. Die in den Beiträgen enthaltenen anschauliche Anwendungsbeispiele aus dem Bereich der Automation setzen die Ergebnisse in den direkten Anwendungsbezug

Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents

207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic

Big data reference architecture for industry 4.0: including economic and ethical Implications

El rápido progreso de la Industria 4.0 se consigue gracias a las innovaciones en varios campos, por ejemplo, la fabricación, el big data y la inteligencia artificial. La tesis explica la necesidad de una arquitectura del Big Data para implementar la Inteligencia Artificial en la Industria 4.0 y presenta una arquitectura cognitiva para la inteligencia artificial - CAAI - como posible solución, que se adapta especialmente a los retos de las pequeñas y medianas empresas. La tesis examina las implicaciones económicas y éticas de esas tecnologías y destaca tanto los beneficios como los retos para los países, las empresas y los trabajadores individuales. El "Cuestionario de la Industria 4.0 para las PYME" se realizó para averiguar los requisitos y necesidades de las pequeñas y medianas empresas. Así, la nueva arquitectura de la CAAI presenta un modelo de diseño de software y proporciona un conjunto de bloques de construcción de código abierto para apoyar a las empresas durante la implementación. Diferentes casos de uso demuestran la aplicabilidad de la arquitectura y la siguiente evaluación verifica la funcionalidad de la misma.The rapid progress in Industry 4.0 is achieved through innovations in several fields, e.g., manufacturing, big data, and artificial intelligence. The thesis motivates the need for a Big Data architecture to apply artificial intelligence in Industry 4.0 and presents a cognitive architecture for artificial intelligence – CAAI – as a possible solution, which is especially suited for the challenges of small and medium-sized enterprises. The work examines the economic and ethical implications of those technologies and highlights the benefits but also the challenges for countries, companies and individual workers. The "Industry 4.0 Questionnaire for SMEs" was conducted to gain insights into smaller and medium-sized companies’ requirements and needs. Thus, the new CAAI architecture presents a software design blueprint and provides a set of open-source building blocks to support companies during implementation. Different use cases demonstrate the applicability of the architecture and the following evaluation verifies the functionality of the architecture

Model-based condition and process monitoring based on socio-cyber-physical systems

Die produzierende Industrie strebt im Rahmen der vierten industriellen Revolution, Industrie 4.0, die Optimierung der klassischen Zielgrößen Qualität, Kosten und Zeit sowie Ressourceneffizienz, Flexibilität, Wandlungsfähigkeit und Resilienz in globalen, volatilen Märkten an. Im Mittelpunkt steht die Entwicklung von Smart Factories, in denen sich relevante Objekte, Produktions-, Logistik- und Informationssysteme sowie der Mensch vernetzen. Cyber-physische Systeme (CPS) tragen als sensorisierte und aktorisierte, resiliente und intelligente Gesamtsysteme dazu bei, Produktionsprozesse und -maschinen sowie die Produktqualität zu kontrollieren. Vordergründig wird die technische Komplexität von Produktionssystemen und damit auch zugehöriger Instandhaltungsprozesse durch die Anforderungen an deren Wandlungsfähigkeit und den zunehmenden Automatisierungsgrad ansteigen. Heraus-forderungen bei der Entwicklung und Implementierung von CPS liegen in fehlenden Interoperabilitäts- und Referenzarchitekturkonzepten sowie der unzureichend definierten Interaktion von Mensch und CPS begründet. Sozio-cyber-physische Systeme (Sozio-CPS) fokussieren die bidirektionale Interaktion von Mensch und CPS und adressieren diese Problemstellung. Gegenstand und Zielstellung dieser Dissertationsschrift ist die Definition von Sozio-CPS in der Domäne der Zustands- und Prozessüberwachung von Smart Factories. Untersucht werden dabei Nutzungsszenarien von Sozio-CPS, die ganzheitliche Formulierung von Systemarchitekturen sowie die Validierung der entwickelten Lösungsansätze in industriellen Anwendungsszenarien. Eine erfolgreiche Umsetzung von Sozio-CPS in drei heterogenen Validierungsszenarien beweist die Korrektheit und Anwendbarkeit der Lösungsansätze.Within the scope of the fourth industrial revolution, Industry 4.0, the manufacturing industry is trying to optimize the traditional target figures of quality, costs and time as well as resource efficiency, flexibility, adaptability and resilience in volatile global markets. The focus is on the development of smart factories, in which relevant objects and humans are interconnected . Cyber-physical systems (CPS) are used as sensorized and actuatorized, resilient and intelligent overall systems to control production processes, machines and product quality . The technical complexity of production systems and their associated maintenance processes are rising due to the demands on their adaptability and the increasing automation. Challenges in the development and implementation of CPS include the lack of interoperability and reference architecture concepts as well as the insufficiently defined interaction of people and CPS. Socio-cyber-physical systems (Socio-CPS) focus on bidirectional interaction of humans and CPS to address this problem. The scope and objective of this dissertation is to define Socio-CPS in the condition and process monitoring of smart factories. This dissertation utilizes scenarios of Socio-CPS, holistically defines system architectures and validates the solutions developed in industrial applications. The successful implementation of Socio-CPS in three heterogeneous validation scenarios proves the correctness and applicability of the solutions

Digital twin model of two-arm collaborative robot for human arms motion simulation using reverse engineering

This study proposes a digital twin (DT) model for a two-arm collaborative robot that can be deployed to simulate human arm motions using the reverse engineering process. A collaborative robot named ABB Yumi – IRB14000 was considered for this study. The purpose of the experiment was to find the best version of the digital twin model by applying translation and rotation constraints in every part of the CAD model of the robot. After adding features to the robot part files, Virtual Reality Modeling Language (VRML) format files were being created to assemble it in 3D world Editor for DT formation and a grid layout was created that contained the control panel of the collaborative model digital twin to connect it with the real world. Finally, a cyber-physical system (CPS) interface was built to replicate human motion. Deep reinforcement learning will be implemented using these two models for human motion simulation

Digital twins: a survey on enabling technologies, challenges, trends and future prospects

Digital Twin (DT) is an emerging technology surrounded by many promises, and potentials to reshape the future of industries and society overall. A DT is a system-of-systems which goes far beyond the traditional computer-based simulations and analysis. It is a replication of all the elements, processes, dynamics, and firmware of a physical system into a digital counterpart. The two systems (physical and digital) exist side by side, sharing all the inputs and operations using real-time data communications and information transfer. With the incorporation of Internet of Things (IoT), Artificial Intelligence (AI), 3D models, next generation mobile communications (5G/6G), Augmented Reality (AR), Virtual Reality (VR), distributed computing, Transfer Learning (TL), and electronic sensors, the digital/virtual counterpart of the real-world system is able to provide seamless monitoring, analysis, evaluation and predictions. The DT offers a platform for the testing and analysing of complex systems, which would be impossible in traditional simulations and modular evaluations. However, the development of this technology faces many challenges including the complexities in effective communication and data accumulation, data unavailability to train Machine Learning (ML) models, lack of processing power to support high fidelity twins, the high need for interdisciplinary collaboration, and the absence of standardized development methodologies and validation measures. Being in the early stages of development, DTs lack sufficient documentation. In this context, this survey paper aims to cover the important aspects in realization of the technology. The key enabling technologies, challenges and prospects of DTs are highlighted. The paper provides a deep insight into the technology, lists design goals and objectives, highlights design challenges and limitations across industries, discusses research and commercial developments, provides its applications and use cases, offers case studies in industry, infrastructure and healthcare, lists main service providers and stakeholders, and covers developments to date, as well as viable research dimensions for future developments in DTs

Improve the Performance of Industrial Agents using Fog Computing

In the last decade, the market requirements have been increasing by demanding numerous different products being highly customizable. Given this need, the necessity for dynamic and flexible production lines are a high priority to meet this change. A traditional approach is not enough to meet the market demand and due to this, several paradigms have been coined out to try and solve this problem. The proposed approach is related to communication between the shop-floor modules in order to create different products. This work proposes an architecture where an integration layer will join a Multiagent System capable of the more recent production paradigms with legacy hardware that is present in the more traditional factories in order to have different products being produced in the same production line. This architecture that revolves an interface that can be used by the agents in the factory in order to use the hardware modules to create a different product if need be. The main features of this project is the fact that by using datamodels and an interface created, it can be easily plugged new stations with different tools to modify the product thus increasing the amount of products that can be created