Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Seaport Data Space for Improving Logistic Maritime Operations

[EN] The maritime industry expects several improvements to efficiently manage the operation processes by introducing Industry 4.0 enabling technologies. Seaports are the most critical point in the maritime logistics chain because of its multimodal and complex nature. Consequently, coordinated communication among any seaport stakeholders is vital to improving their operations. Currently, Electronic Data Interchange (EDI) and Port Community Systems (PCS), as primary enablers of digital seaports, have demonstrated their limitations to interchange information on time, accurately, efficiently, and securely, causing high operation costs, low resource management, and low performance. For these reasons, this contribution presents the Seaport Data Space (SDS) based on the Industrial Data Space (IDS) reference architecture model to enable a secure data sharing space and promote an intelligent transport multimodal terminal. Each seaport stakeholders implements the IDS connector to take part in the SDS and share their data. On top of SDS, a Big Data architecture is integrated to manage the massive data shared in the SDS and extract useful information to improve the decision-making. The architecture has been evaluated by enabling a port authority and a container terminal to share its data with a shipping company. As a result, several Key Performance Indicators (KPIs) have been developed by using the Big Data architecture functionalities. The KPIs have been shown in a dashboard to allow easy interpretability of results for planning vessel operations. The SDS environment may improve the communication between stakeholders by reducing the transaction costs, enhancing the quality of information, and exhibiting effectivenessThis work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the PIXEL Port Project under Grant 769355, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), EcuadorSarabia-Jácome, D.; Palau Salvador, CE.; Esteve Domingo, M.; Boronat, F. (2019). Seaport Data Space for Improving Logistic Maritime Operations. IEEE Access. 8:4372-4382. https://doi.org/10.1109/ACCESS.2019.2963283S43724382

An Edge-Cloud based Reference Architecture to support cognitive solutions in Process Industry

Process Industry is one of the leading sectors of the world economy, characterized however by intense environmental impact, and very high-energy consumption. Despite a traditional low innovation pace in PI, in the recent years a strong push at worldwide level towards the dual objective of improving the efficiency of plants and the quality of products, significantly reducing the consumption of electricity and CO2 emissions has taken momentum. Digital Technologies (namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies) are enabling drivers for a Twin Digital-Green Transition, as well as foundations for human centric, safe, comfortable and inclusive workplaces. Currently, digital sensors in plants produce a large amount of data, which in most cases constitutes just a potential and not a real value for Process Industry, often locked-in in close proprietary systems and seldomly exploited. Digital technologies, with process modelling-simulation via digital twins, can build a bridge between the physical and the virtual worlds, bringing innovation with great efficiency and drastic reduction of waste. In accordance with the guidelines of Industrie 4.0 this work proposes a modular and scalable Reference Architecture, based on open source software, which can be implemented both in brownfield and greenfield scenarios. The ability to distribute processing between the edge, where the data have been created, and the cloud, where the greatest computational resources are available, facilitates the development of integrated digital solutions with cognitive capabilities. The reference architecture is being validated in the three pilot plants, paving the way to the development of integrated planning solutions, with scheduling and control of the plants, optimizing the efficiency and reliability of the supply chain, and balancing energy efficiency

Framework for IoT Service Oriented Systems

The forth industrial revolution is here, and with it Industry 4.0, which translates in many changes to the industry. With the introduction of paradigms like Internet of Things, Cyber Physical Systems or Cloud Computing, the so called Smart Factories are becoming a main part of today’s manufacturing systems. The vf-OS Project, where this thesis falls, intends to be an Open Operating System for Virtual Factories where the overall network of a collaborative manufacturing and logistics environment can be managed and thus enabling humans, applications and devices to communicate and interoperate in an interconnected operative environment. This thesis intends to contribute to the vision that any kind of sensor or actuator plugged to the virtual factory network, becomes promptly accessible in the operative environment and the services that it provides can be accessed and used by any API composing the system. Finally, it also aims to prove that an IoT Service Oriented Sys-tem constituted of open software components can be of great assistance and provide numerous contributions to the emerging Industry 4.0 and consequently to the Factories of the Future. With that aim, this thesis will focus on the development of two out of five inter-connected applications that answer not only to different use case scenarios presented in the vf-OS but also provide solutions to answer a practical agriculture scenario, which uses mainly IoT devices and other cutting-edge technologies like cloud compu-ting and FIWARE

Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE

Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion

The European Industrial Data Space (EIDS)

This research work has been performed in the framework of the Boost 4.0 Big Data lighthouse initiative, a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 780732. This datadriven digital transformation research is also endorsed by the Digital Factory Alliance (DFA)The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.publishersversionpublishe

FIWARE-based application for control of Smart Cities

Esta tesis es un estudio teórico sobre el framework FIWARE, su ecosistema y sus aplicaciones prácticas. Primero se hace una descripción de FIWARE como ecosistema, los principios en los que está basado, así como su misión y un histórico de su implementación. Después se detallan los programas que forman el ecosistema y su comunidad. En la parte técnica, se describe, con el uso de ejemplos, la tecnología que utilizan los distintos componentes que forman FIWARE y el mercado en el que adquirir las soluciones. Por último se muestran algunos casos de éxito de la implementación de FIWARE.This thesis is a theoretical study about the FIWARE framework, its ecosystem and its practical applications. First, a description of FIWARE as an ecosystem and the principles it is based on, as well as its mission and a timeline of its implementation is done. Then, the main programs and the community that form the ecosystem are detailed. On the technical section, it is described, with the use of examples, the technology employed in each FIWARE component and the market where the solutions can be acquired. Finally, some success stories are shown where FIWARE was implemented.Grado en Ingeniería Informátic

A Framework for Service-Oriented Architecture (SOA)-Based IoT Application Development

Funding: This research was partially supported by funds provided by the European Commission in the scope of FoF/H2020-723710 vf-OS, ICT/H2020-825631 ZDMP projects, and by the FCT— Fundação para a Ciência e a Tecnologia in the scope of UIDB/00066/2020 related to CTS—Centro de Tecnologia e Sistemas research unit.In the last decades, the increasing complexity of industrial information technology has led to the emergence of new trends in manufacturing. Factories are using multiple Internet of Things (IoT) platforms to harvest sensor information to improve production. Such a transformation contributes to efficiency growth and reduced production costs. To deal with the heterogeneity of the services within an IoT system, Service-Oriented Architecture (SOA) is referred to in the literature as being advantageous for the design and development of software to support IoT-based production processes.The aim of SOA-based design is to provide the leverage to use and reuse loosely coupled IoT services at the middleware layer to minimise system integration problems. We propose a system architecture that follows the SOA architectural pattern and enables developers and business process designers to dynamically add, query or use instances of existing modular software in the IoT context. Furthermore, an analysis of utilization of modular software that presents some challenges and limitations of this approach is also in the scope of this workpublishersversionpublishe

IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water.

[EN] The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods.FundingThis research was funded by Spanish Ministerio de Economia y Competitividad, Gobierno de Espana, grant number DPI2016-80303-C2-1-P.Campelo Rivadulla, JC.; Capella Hernández, JV.; Ors Carot, R.; Peris Tortajada, M.; Bonastre Pina, AM. (2022). IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water. Sensors. 22(3):1-16. https://doi.org/10.3390/s2203084211622