214 research outputs found
Empowering a Cyber-Physical System for a Modular Conveyor System with Self-organization
The Industry 4.0 advent, advocating the digitalization and transformation of current production systems towards the factories of future, is introducing significant social and technological challenges. Cyber-physical systems (CPS) can be used to realize these Industry 4.0 compliant systems, integrating several emergent technologies, such as Internet of Things, big data, cloud computing and multi-agent systems. The paper analyses the advantages of using biological inspiration to empower CPS, and particularly those developed using distributed and intelligent paradigms such as multi-agent systems technology. For this purpose, the self-organization capability, as one of the main drivers in this industrial revolution is analysed, and the way to translate it to solve complex industrial engineering problems is discussed. Its applicability is illustrated by building a self-organized cyber-physical conveyor system composed by different individual modular and intelligent transfer modules.info:eu-repo/semantics/publishedVersio
Security for a multi-agent cyber-physical conveyor system using machine learning
One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.info:eu-repo/semantics/publishedVersio
Simulation and Control of a Cyber-Physical System under IEC 61499 Standard
IEC 61499 standard provides an architecture for control systems using function blocks (FB), languages, and semantics. These devices can be interconnected and communicate with each other. Each device contains several resources and algorithms with a communication FB at the end, which can be created, configured, and deleted without affecting other resources. Physical element can be represented by a FB that encapsulates the functionality (data/events, process, return data/events) in a single module that can be reused and combined. This work presents a simplified implementation of a modular control system using a low-cost device. In the prototyping of the application, we use 4diac to control, model and validate the implementation of the system on a programmable logic controller. It is proved that this approach can be used to model and simulate a cyber-physical system as a single element or in a networked combination. The control models provide a reusable FB design.We acknowledge the financial support of CIDEM, R&D
unit funded by FCT – Portuguese Foundation for the
Development of Science and Technology, Ministry of
Science, Technology and Higher Education, under the Project
UID/EMS/0615/2019, and it was supported by FCT, through
INEGI and LAETA, under project UIDB/50022/2020.info:eu-repo/semantics/publishedVersio
Self-organized cyber-physical conveyor system using multi-agent systems
The adoption of industrial cyber-physical systems is facing several challenges, with artificial
intelligence and self-organization techniques assuming critical aspects to be considered
in the deployment of such solutions to support the dynamic evolution and adaptation
to condition changes. This paper describes the implementation of a modular, flexible and
self-organized cyber-physical conveyor system build up with different individual modular
and intelligent transfer modules. For this purpose, multi-agent systems are used to distribute
intelligence among transfer modules supporting pluggability and modularity, complemented
with self-organization capabilities to achieve a truly self-reconfigurable system.
Furthermore, Internet of Things and Artificial Intelligence technologies are used to enable
the real-time monitoring of the system, aiming the detection and prevention of anomalies in
advance, and to enable the protection from possible external threats.info:eu-repo/semantics/publishedVersio
Development of security mechanisms for a multi-agent cyber-physical conveyor system using machine learning
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáOne main foundation of the Industry 4.0 is the connectivity of devices and systems using
Internet of Things technologies, where Cyber-physical systems (CPS) act as the backbone
infrastructure based on distributed and decentralized structures. CPS requires the use of
Artificial Intelligence (AI) techniques, such as Multi-Agent Systems (MAS), allowing the
incorporation of intelligence into the CPS through autonomous, proactive and cooperative
entities. The adoption of this new generation of systems in the industrial environment
opens new doors for various attacks that can cause serious damage to industrial production
systems.
This work presents the development of security mechanisms for systems based on MAS,
where these mechanisms are used in an experimental case study that consists of a multiagent
cyber-physical conveyor system. For this purpose, simple security mechanisms were
employed in the system, such as user authentication, signature and message encryption,
as well as other more complex mechanisms, such as machine learning techniques that
allows the agents to be more intelligent in relation to the exchange of messages protecting
the system against attacks, through the classification of the messages as reliable or not,
and also an intrusion detection system was carried out.
Based on the obtained results, the efficient protection of the system was reached,
mitigating the main attack vectors present in the system architecture.Uma das principais bases da Indústria 4.0 é a conectividade de dispositivos e sistemas
utilizando as tecnologias da Internet das Coisas, onde os sistemas ciber-físicos atuam
como a infraestrutura principal com base em estruturas distribuídas e descentralizadas.
Os sistemas ciber-físicos requerem o uso de técnicas de Inteligência Artificial, como por
exemplo, Sistemas Multi-Agentes, permitindo a incorporação de inteligência nos sistemas
ciber-físicos através de entidades autônomas, proativas e cooperativas. A adoção dessa
nova geração de sistemas no ambiente industrial abre novas portas para vários ataques
que podem causar sérios danos aos sistemas de produção industrial.
Este trabalho apresenta o desenvolvimento de mecanismos de segurança para sistemas
baseados em sistemas multi-agentes, em que esses mecanismos são utilizados em um caso
de estudo experimental que consiste em um sistema de transporte ciber-físico baseado em
sistemas multi-agentes. Para isso, mecanismos simples de segurança foram empregados
no sistema, como autenticação do usuário, assinatura e criptografia de mensagens, além
de outros mecanismos mais complexos, como técnicas de aprendizagem de máquina, que
permite que os agentes sejam mais inteligentes em relação à troca de mensagens, protegendo
o sistema contra ataques, através da classificação das mensagens como confiáveis
ou não, e também foi realizado um sistema de detecção de intrusões.
Com base nos resultados obtidos, obteve-se uma proteção eficiente do sistema, mitigando
os principais vetores de ataque presentes na arquitetura do sistema
Conceptualising Assembly 4.0 through the Drone Factory
This paper aims to discuss the complexity of designing an assembly system according to industry 4.0. This is done by introducing the drone factory as a learning facility at the digital innovation hub SIILab. The paper discusses the areas of Operator-Organisation, Operator-Technologies, Technologies-Product and Product-Organisation in a current state and information support subsystem, IIoT architecture and hardware in the assembly 4.0 context
Komponenttien luokittelu ja parhaat käytännöt tuotantosimulaation mallinnuksessa
Production simulation software plays a major role in validation, optimization and illustration of production systems. Operation of production simulation is generally based on components and their interaction. Components typically represent factory floor devices, but in addition, there can be components to provide visualization, statistics, control or other input to simulation. The demand for having high-quality, easy-to-use and compatible components emphasizes the importance of component modelling.
The objectives of this thesis were to develop component classes based on industrial devices, to standardize component modelling solutions and best practices in component modelling. Other objectives were to identify and analyse future prospects of production simulation. This focuses on the concept of digital twin, which could be described as reflective real-time simulation model from the physical system. In addition, focus is also set on formal modelling languages.
The outcome of this thesis presents component classes and best practices in component modelling. In component classification, the focus was set to development of generic components, which can be controlled with signal-based logic. This enables components from the software to be externally controlled. In addition, automatic model creation tool wizard, is implemented to instantly generate components based on the defined component classes. Best practices were based on the selected modelling fields that are most relevant for general use. In the development of best practices, interviewing method was utilized to receive input from simulation experts.Tuotantosimulaatio on tärkeässä osassa tuotantojärjestelmien validoinnissa, optimoinnissa ja visualisoinnissa. Tuotantosimulaation toiminta perustuu yleisesti komponentteihin ja niiden väliseen vuorovaikutukseen. Komponentit esittävät tyypillisesti tehtaasta löytyviä laitteita ja esineitä, mutta komponentteja voidaan käyttää myös visualisointiin, statistiikan keräämiseen, järjestelmän ohjaukseen tai muuhun tarpeeseen simuloinnissa.
Tämän diplomityön tavoitteita oli kehittää komponenttiluokkia teollisuudesta valittujen laitteiden perusteella, mikä mahdollistaa mallinnusratkaisujen standardoinnin. Sen lisäksi tavoitteena oli kehittää parhaat käytännöt komponenttimallinnukseen. Muita tavoitteita oli tunnistaa ja analysoida tulevaisuuden näkymiä tuotantosimulaatiolle. Tämä keskittyi pääosin digitaaliseen kaksoseen, jota voidaan kuvata reaaliaikaisesti peilautuvaksi simulaatiomalliksi todellisesta järjestelmästä. Tämän lisäksi työssä keskityttiin formaaleihin mallinnuskieliin.
Diplomityön lopputulos esittää kehitetyt komponenttiluokat ja parhaat käytännöt komponenttimallinnuksessa. Komponenttien luokittelussa keskityttiin kehittämään geneerisiä komponentteja, joita voidaan ohjata signaalipohjaisilla komennoilla. Tämä mahdollistaa komponentin ohjaamisen myös simulointiohjelman ulkopuolelta. Tämän lisäksi automaattista komponenttien luomistyökalua käytettiin luokiteltujen komponenttien luomisessa. Parhaat käytännöt komponenttimallinnuksessa pohjautuivat mallinnuksen oleellisimpiin osa-alueisiin tavanomaisissa mallinnustilanteissa. Parhaiden käytäntöjen kehityksessä haastateltiin simulointiammattilaisia, joiden mielipiteistä muodostettiin perusta käytäntöjen kehitykselle
Komponenttien luokittelu ja parhaat käytännöt tuotantosimulaation mallinnuksessa
Production simulation software plays a major role in validation, optimization and illustration of production systems. Operation of production simulation is generally based on components and their interaction. Components typically represent factory floor devices, but in addition, there can be components to provide visualization, statistics, control or other input to simulation. The demand for having high-quality, easy-to-use and compatible components emphasizes the importance of component modelling.
The objectives of this thesis were to develop component classes based on industrial devices, to standardize component modelling solutions and best practices in component modelling. Other objectives were to identify and analyse future prospects of production simulation. This focuses on the concept of digital twin, which could be described as reflective real-time simulation model from the physical system. In addition, focus is also set on formal modelling languages.
The outcome of this thesis presents component classes and best practices in component modelling. In component classification, the focus was set to development of generic components, which can be controlled with signal-based logic. This enables components from the software to be externally controlled. In addition, automatic model creation tool wizard, is implemented to instantly generate components based on the defined component classes. Best practices were based on the selected modelling fields that are most relevant for general use. In the development of best practices, interviewing method was utilized to receive input from simulation experts.Tuotantosimulaatio on tärkeässä osassa tuotantojärjestelmien validoinnissa, optimoinnissa ja visualisoinnissa. Tuotantosimulaation toiminta perustuu yleisesti komponentteihin ja niiden väliseen vuorovaikutukseen. Komponentit esittävät tyypillisesti tehtaasta löytyviä laitteita ja esineitä, mutta komponentteja voidaan käyttää myös visualisointiin, statistiikan keräämiseen, järjestelmän ohjaukseen tai muuhun tarpeeseen simuloinnissa.
Tämän diplomityön tavoitteita oli kehittää komponenttiluokkia teollisuudesta valittujen laitteiden perusteella, mikä mahdollistaa mallinnusratkaisujen standardoinnin. Sen lisäksi tavoitteena oli kehittää parhaat käytännöt komponenttimallinnukseen. Muita tavoitteita oli tunnistaa ja analysoida tulevaisuuden näkymiä tuotantosimulaatiolle. Tämä keskittyi pääosin digitaaliseen kaksoseen, jota voidaan kuvata reaaliaikaisesti peilautuvaksi simulaatiomalliksi todellisesta järjestelmästä. Tämän lisäksi työssä keskityttiin formaaleihin mallinnuskieliin.
Diplomityön lopputulos esittää kehitetyt komponenttiluokat ja parhaat käytännöt komponenttimallinnuksessa. Komponenttien luokittelussa keskityttiin kehittämään geneerisiä komponentteja, joita voidaan ohjata signaalipohjaisilla komennoilla. Tämä mahdollistaa komponentin ohjaamisen myös simulointiohjelman ulkopuolelta. Tämän lisäksi automaattista komponenttien luomistyökalua käytettiin luokiteltujen komponenttien luomisessa. Parhaat käytännöt komponenttimallinnuksessa pohjautuivat mallinnuksen oleellisimpiin osa-alueisiin tavanomaisissa mallinnustilanteissa. Parhaiden käytäntöjen kehityksessä haastateltiin simulointiammattilaisia, joiden mielipiteistä muodostettiin perusta käytäntöjen kehitykselle
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
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