Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies

This paper proposes a methodology for designing decision support systems for visualising and mitigating the Internet of Things cyber risks. Digital technologies present new cyber risk in the supply chain which are often not visible to companies participating in the supply chains. This study investigates how the Internet of Things cyber risks can be visualised and mitigated in the process of designing business and supply chain strategies. The emerging DSS methodology present new findings on how digital technologies affect business and supply chain systems. Through epistemological analysis, the article derives with a decision support system for visualising supply chain cyber risk from Internet of Things digital technologies. Such methods do not exist at present and this represents the first attempt to devise a decision support system that would enable practitioners to develop a step by step process for visualising, assessing and mitigating the emerging cyber risk from IoT technologies on shared infrastructure in legacy supply chain systems

System architectures for Industrie 4.0 applications

Industrie 4.0 principles demand increasing flexibility and modularity for automated production systems. Current system architectures provide an isolated view of specific applications and use cases, but lack a global, more generic approach. Based on the specific architectures of two EU projects and one German Industrie 4.0 project, a generic system architecture is proposed. This system architecture features the strengths of the three isolated proposals, such as cross-enterprise data sharing, service orchestration, and real-time capabilities, and can be applied to a wide field of applications. Future research should be directed towards considering the applicability of the architecture to other equal applications.info:eu-repo/semantics/publishedVersio

IIoT Based Efficiency Optimization in Logistics Applications

The Industrial Internet of Thing (IIoT) approach to an Industry plant design, devises a comprehensive interconnection of the system components, from sections up to single devices, in order to get a general and punctual understanding of the process. Such an intelligent network, mostly based on Ethernet basic layers, when properly conceived, should be able to add relevant value to the plant operation. This paper shows how, within the IIoT frame topics, the plant efficiency can be addressed and bring relevant improvement. The reason is that variables directly related to the energy consumption, such as current, electric power, actuator and motor torque, speed, etc., can be timely and easily monitored in the entire plant, since they are already conveyed on the network, due to real time control and diagnostics purpose. A power consumption diagram can be derived, and give hints on how to optimize operations, based on some efficiency index. The paper, after a general discussion, proves it with practical examples based on a Gantry robot, driven in an EtherCAT based automation network, and on the stacker cranes of an automated warehouse

Data science on industrial data -- Today's challenges in brown field applications

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls

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

Plant-wide interoperability and decoupled, data-driven process control with message bus communication

Conventional industrial communication systems suffer from rigidness, inflexibility and lack of scalability. The environment is heterogeneous as the systems exchange data with a variety communication protocols, some of which are proprietary. This makes it laborious and expensive to reconfigure or upgrade the systems. As the solution, this article proposes a message-bus-based communication architecture to enable information exchange between systems regardless of their geographical location and position within the functional hierarchy of the plant. The architecture not only enables communication to cross the conventional physical borders but also provides scalability to growing data volumes and network sizes. As proofs of concept, the article presents a prototype in three environments: a copper smelter, a steel plant and a distillation column. The results suggest that the message-bus-based approach has potential to renew industrial communications, a core part of the fourth industrial revolution.H2020, 723661, COCO