Ein wichtiger Treiber des starken Datenwachstums ist die Industrie 4.0 und damit untrennbar verbunden das “Internet der Dinge”. Durch das Internet wachsen reale und virtuelle Welt zum Internet der Dinge zusammen. Im Bereich Fertigung sind Maschinen, Produktionsanlagen und Lagersysteme zunehmend in der Lage, selbstständig Informationen auszutauschen, Aktionen anzustoßen und einander zu steuern. Ziel ist es, Prozesse in den Bereichen Entwicklung und Konstruktion, Fertigung und Service signifikant zu verbessern. Diese vierte industrielle Revolution steht für die Verknüpfung von industrieller Fertigung und Informationstechnologie – und damit für eine neue Stufe an Effizienz und Effektivität. Mit Industrie 4.0 entstehen neue Informationsräume, die ERP-Systeme, Datenbanken, das Internet sowie Echtzeitinformationen aus Fabriken, Lieferketten und Produkten miteinander verbinden.
In der Data Innovation Community “Industrie 4.0” sollen wichtige datengetriebene Aspekte der vierten industriellen Revolution erforscht werden, bspw. die vorausschauende Wartung von Produktionsressourcen oder auch das Auffinden von Anomalien in Produktionsprozessen.
Die Data Innovation Community “Industrie 4.0” richtet sich daher an alle interessierten Unternehmen und Forschungseinrichtungen, die bzgl. dieser Aspekte gemeinsame Forschung betreiben wollen. Dies schließt sowohl Anwenderunternehmen mit ein als auch Unternehmen der Automatisierungsbranche sowie der IT-Branche.
Plamen Kiradjiev
kiradjiev@de.ibm.com
IBM
Dr. Tilman Becker
tilman.becker@dfki.de
Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Zu einer Mitarbeit in der Data Innovation Community oder Teilnahme an deren Treffen kontaktieren Sie bitte das DIC per E-Mail unter industrie@sdil.de.
Mitglieder können auf die internen Foren im SAP Jam zugreifen.
Published June 2020. A large amount of data is generated in modern e-commerce systems – especially when department stores fill their large warehouses and products from supplier lists are to be integrated into the in-house product information system. On the way from the supplier list into the Product Information Management (PIM) system, data is […]
The core idea of the collaborative research project “Risk Management 4.0” is the exploratory analysis regarding the possibility of machine learning for predictive causal analysis of risk factors across investment portfolios. Such automated predictive risk analysis is also in demand by the German Federal Financial Supervisory Authority (BaFin) in its consultation paper of July 16, […]
Published November 2019. EDI provided the SDSC-BW with around 1,000 technical drawings in PDF format. In these technical drawings, different standards are used for surface specifications, dimensions and references. The fonts of the product descriptions are not uniform and are often supplemented by handwritten notes due to changes in the product descriptions. The work of […]
Published November 2019. Downtimes of machines and systems in manufacturing are expensive and should be avoided as far as possible. Process monitoring methods are suitable for detecting deviations from regular plant operation. As a result, malfunctions can be detected and remedied at an early stage. The challenge here lies in identifying the position of the […]
Published November 2019. The ability to make decisions autonomously and act on them is a core theme of artificial intelligence. Nevertheless, transport drones today are not able to detect and correct errors in their system autonomously. This leads to drone crashes and can result in high property damage and personal injury. To ensure the functional […]
Publiziert November 2019. Downtimes of machines and equipment in manufacturing are expensive and should be avoided as far as possible. A frequent cause of machine downtime is damage to bearings due to wear, corrosion or overload. These causes of damage were detected using high-frequency acceleration and structure-borne sound sensors. This information can be further used […]
Published November 2019. The core idea of the project is to develop a technology for the dynamic detection of so-called “intents” for dialog systems, which can be determined “online”, i.e. in this case during the operation of a service hotline. For example, a user may report that a certain service is no longer working, that […]
Published November 2019. The project partner SIS has been commercially active in the field of people flow analysis at major events for many years. Here, for example, smartphone apps are used to record movement data of visitors to the event and evaluate it in real time in order to provide rescue forces, for example, with […]
Published June 2019. The data provided by ArtiMinds came from an experiment in which a robotic arm searches for holes to place a component on an electronic circuit board. The experiment consisted of 120 runs that generated a total of 2,487 observations. For each observation, the robot arm’s start and end positions, force, and run […]
Published April 2019. Schlötter provided SDSC-BW’s data analytics experts with sales data from the past 13 years: a total of about one million data elements. The data included sales information for each product on each day, such as sales volume, warehouse address, customer number, order time, delivery time, etc. To reduce the losses caused by […]