Industrie 4.0

Data analytics for the fourth industrial revolution, such as proactive service and maintenance of production resources or finding anomalies in production processes.



Data-driven aspects of medicine are explored, such as the need-driven care of patients or IT controlled medical technology.


Smart Infrastructure

Untersuchung datengetriebener Aspekter städtischen Lebens, bspw. der Verkehrssteuerung, der Müllentsorgung oder der Katastrophenbewältigung, bedarfsgesteuerte Optimierung von Verbrauchsmodellen, basierend auf Daten intelligenter Stromzähler.

Featured Projects

  • VDAR

    VDAR: Distributed Decentralized Autonomous Control Systems for Distributed Energy Markets

    The VDAR-project goes back to the framework project “Software Campus” supported by the Federal Ministry of Education and Research (BMBF). Within the scope of the VDAR-research project,control concepts have been explored that combine the economic system of the electricity market and the physical system of the electricity grid in a decoupled control circuit. The aim is to ultimately improve the availability of energy.

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  • SDI-X: Smart Data Innovation Processes, Tools, and Operational Concepts

    The BMBF-funded project “Smart Data Innovation processes, tools and operational concepts (SDI-X)” explores appropriate tools and best practices. The aim is to not only facilitate extensive data analysis projects between different research and industry partners but also enable their prompt implementation. The results of the projects will be fully integrated into the SIDL and its projects.

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    Smart Data Solutions for Producing SMEs in Baden-Württemberg

    The project is funded by the Ministry of science, research and art Baden-Württemberg (MWK) as part of the Smart Data Solution Center Baden-Württemberg (SDSC BW) to explore the use of suitable Smart Data technologies for producing SMEs. The project’s aim is the research of a simplified access to Smart Data technologies to facilitate the use of these technologies for SMEs. The results of the conducted Smart Data analyses of real industrial data sets are published in the form of public success stories.

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  • SmartFactoryKL

    Predictive Maintenance Data Analysis on SmartFactoryKL-generated Data

    The joint research project sought by SDIL and SmartFactoryKL is strongly related to the topics “Industry 4.0” and “Internet of Things (IoT)”. Modern machinery is characterized by a large amount of sensors that continuously offer such status information that is relevant to the production process. The intelligent monitoring, storage, and analysis of sensory data can have multiple positive consequences. Predictive maintenance will be one of the key issues for the future development of highly modular, multi-vendor production systems.

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    Association Rule Mining for Data-driven Services based on Industrial Logs

    Today’s industrial plants continuously produce log data on the references of measurements, error reports, and documented user interventions. The existing solutions show some constraints, though. The main focus of this project is to use the potential that sources in the analysis of log files in relation to the system level along the entire production or process context. The identification of cause-effects relationships at the system level would allow an optimization of industrial plants and corresponding processes.

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