Industrial internet and its role in process automation

Modern process automation undergoes a major shift in the way it addresses conventional challenges. Moreover, it is adapting to the newly arising challenges due to changing business scenarios. Nowadays, the areas of the automation that recently were rather separate start to merge and the border between them is fading. This situation only adds struggle to the already highly competitive production industry. In order to be successful, companies should adopt new approaches to the way their processes are automated, controlled, and managed. One of these approaches is the so-called Industrial Internet. It is the next step after the traditional paradigm of the process automation pyramid that leads to the new vision of interconnected processes, services, machines and people. However, general company does not usually eager to implement the new technology to its business. One of the reasons for this is that it does not see the advantages that the Industrial Internet brings. This is due to the lack of sufficient number of successful implementation examples in various industrial areas and of clear business scenarios for the use of the Industrial Internet. Aim of the presented thesis is to create a convincing Industrial Internet application scenario. For the implementation, a mineral concentration plant was chosen as one of the industrial premises that possesses the shortage of the Industrial Internet examples. Literature review section describes the process automation state of art. It lists and reviews the research and development initiatives related to the Industrial Internet. Moreover, the Industrial Internet fundamentals are given. Finally, it describes the Industrial Internet applications and the case studies. In the practical part, at first, the description of the mineral concentration plant is given. Then, the next section describes the Industrial Internet application scenario. In the following section technical guidelines for the system implementation are given. Also, in the concluding part of the thesis the future direction of research work are discussed

IoT Middleware Platforms for Smart Energy Systems: An Empirical Expert Survey

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances

Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Industrial Internet of Things

In past few years, almost every industry puts lot of effort in introducing Internet of Things to expand production volume while maintaining low cost and increase the energy efficiency. Several different techniques have been introduced to achieve the goal of real-time and bi-directional communication. However, the problem of scalability and security in the domain of IoT is still to be solved. Absence or maturity level of these two features hinders this technology from its way to factory floor. Moreover, there is no generic solution on a global scale of IoT security and scalability. The main focus of this thesis is to provide big picture of Industrial Internet of Things to readers with different possibilities of IIoT implementation for small and medium sized enterprises. In addition to this, thesis also focuses on working, benefits, disadvantages and comparison between different old and emerging technologies with several use cases, options and practical implementation of IIoT concept on JOT Automation products to build real time, modular, bi-directional, scalable and secure system. The proposed approach is based on maturity level of IoT stack protocols and cloud services. Architecture of system is designed in such a way that it can be integrated to current ERP solution. The results and final application reflects the effectiveness of approach

Engineering framework for service-oriented automation systems

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

Data and Process Mining Applications on a Multi-Cell Factory Automation Testbed

This paper presents applications of both data mining and process mining in a factory automation testbed. It mainly concentrates on the Manufacturing Execution System (MES) level of production hierarchy. Unexpected failures might lead to vast losses on investment or irrecoverable damages. Predictive maintenance techniques, active/passive, have shown high potential of preventing such detriments. Condition monitoring of target pieces of equipment beside defined thresholds forms basis of the prediction. However, monitored parameters must be independent of environment changes, e.g. vibration of transportation equipments such as conveyor systems is variable to workload. This work aims to propose and demonstrate an approach to identify incipient faults of the transportation systems in discrete manufacturing settings. The method correlates energy consumption of the described devices with the workloads. At runtime, machine learning is used to classify the input energy data into two pattern descriptions. Consecutive mismatches between the output of the classifier and the workloads observed in real time indicate possibility of incipient failure at device level. Currently, as a result of high interaction between information systems and operational processes, and due to increase in the number of embedded heterogeneous resources, information systems generate unstructured and massive amount of events. Organizations have shown difficulties to deal with such an unstructured and huge amount of data. Process mining as a new research area has shown strong capabilities to overcome such problems. It applies both process modelling and data mining techniques to extract knowledge from data by discovering models from the event logs. Although process mining is recognised mostly as a business-oriented technique and recognised as a complementary of Business Process Management (BPM) systems, in this paper, capabilities of process mining are exploited on a factory automation testbed. Multiple perspectives of process mining is employed on the event logs produced by deploying Service Oriented Architecture through Web Services in a real multi-robot factory automation industrial testbed, originally used for assembly of mobile phones

Internet of Things : technologies and applications in healthcare management and manufacturing

L'Internet des Objets (ou IoT) s'appuie sur des objets connectés dotés de capteurs et technologies capables d'échanger des données entre eux de manière indépendante. Ces nouvelles technologies offrent aux entreprises et à toutes les organisations des moyens pour l’acquisition et le traitement intelligent de l’information (Industrie 4.0) pour demeurer compétitives. Ce mémoire vise à analyser la contribution de l'IoT dans les soins de santé et production, mettant l'accent sur l'Industrie 4.0 et la maintenance prédictive, particulièrement en maintenance, sur la base d’oeuvres littéraires récentes publiées au cours de la dernière décennie. L’objectif principal de ce mémoire est de comprendre l'IoT, d’exposer ses potentiels et sa stratégie de déploiement dans différents domaines d’applications. Même, le but est de comprendre que l'IoT ne se limite pas à l'application de la maintenance des systèmes de production mais aussi du bien-être des patients, c'est pourquoi j'ai choisi ces deux domaines importants où l'IoT peut être appliqué (santé et production) pour ce travail de recherche. Cette thèse aidera à explorer comment l'IoT transforme le système de santé. J'explique comment l'IoT offre de grandes avancées dans ce système. Je donne quelques exemples où ses concepts souhaiteraient être implémentés pour améliorer la qualité des soins des patients et quelques études récentes. Outre, je clarifie l'impact de l’Industrie 4.0 sur la production, notamment en maintenance, en lien avec la maintenance prédictive rendue possible par l’IoT. Je fournis une vue d'ensemble de l'Industrie 4.0 et de la maintenance prédictive. J’aborde les fonctionnalités de l'Industrie 4.0 et présente ses technologies de pilotage susceptibles d'améliorer les domaines de processus de production, tels que la réduction des temps d'immobilisation, les coûts de service, etc. J'attire l'attention sur les implications de la maintenance prédictive dans l’Industrie 4.0 en décrivant son fonctionnement et comment les fabricants peuvent l'exécuter efficacement, avec des exemples à l'appui.The Internet of Things (or IoT) relies on connected objects embedded with sensors and other technologies capable of exchanging data with each other independently. These new technologies provide businesses and all organizations with the means to acquire and intelligently process information (Industry 4.0) to remain competitive. This thesis aims to analyze the contribution of IoT in healthcare and manufacturing, with a focus on Industry 4.0 and Predictive Maintenance, specifically in maintenance, based on recent literary works published over the last decade. The main purpose of this thesis is to understand what IoT is, to highlight its potentials and its deployment strategy in various areas of application. Similarly, the goal is to understand that IoT is not limited to the application of the maintenance of production systems but also of patients’ wellbeing which is the reason why I selected these two important areas where IoT can be applied (healthcare and manufacturing) for this research work. This thesis will help explore how IoT is transforming the healthcare system. I explain how IoT offers great advances in the healthcare system. I give some examples of where its concepts would like to be implemented to improve the quality of care of patients and some recent studies. In addition, I clarify the impact of Industry 4.0 in manufacturing especially in maintenance, in connection with predictive maintenance made possible by IoT. I provide an overview of Industry 4.0 and predictive maintenance. I discuss the capabilities of Industry 4.0 and present its driving technologies that can improve all areas of production processes such as reducing downtime, service costs , etc. Moreover, I draw attention to the implications of predictive maintenance in Industry 4.0 by describing how it works and how manufacturers can run it effectively, with supporting examples

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