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

  • software_ag-logo

    Ports as Intelligent Logistics Hubs

    This project is part of the Transforming Transport EU lighthouse project. The TransformingTransport project will demonstrate, in a realistic, measurable, and replicable way the transformative effects that Big Data will have to the mobility and logistics market.

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  • Trelleborg_SDIL

    Condition monitoring and prediction of sealing systems

    Trelleborg Sealing Solutions carries out numerous, fully instrumented, tests of these seals and sealing systems. Measured variables, such as pressures, temperatures and velocities, are measured at various points in a very high frequency. The tests carried out and planned represent an extensive data base, which offers great potential for the application of big data analytics.

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  • ibm-logo

    Optimization of product quality at OSRAM Schwabmünchen

    An important goal in the implementation of an industry 4.0 strategy is the optimization of production to further increase the quality of the produced product. Using data analysis of the production parameters, sensor data, test protocols and their interdependencies forecasts and rules for the production can be created.

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  • siemens-logo

    Enhancing Traffic Flow Forecasting with Environmental Models

    In this project, a traffic-flow forecasting method using environmental models is proposed. Nowadays, traffic flow prediction mainly takes into account information from individual, specific sensors. However, information from neighboring sensors and other sensors in the traffic subnet could be used to improve modern prognosis models.

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  • SDSC-BW: Smart data analysis for component manufacturing

    Smart data analyzes support the scheduling of component production at Herrenknecht AG. Within a customer order, it is necessary to produce various components. The core components are generated in individual production orders at the corporate headquarters in Schwanau. Component manufacturing includes cost, planning, production and quality data.

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