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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.

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Medicine

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

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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

  • Zustandsüberwachung und -vorhersage von Dichtungssystemen

    Flugzeug-Fahrwerke stellen erhebliche Anforderungen an Dichtungssysteme. Daher werden diese in umfangreich instrumentierten Tests hohen Belastungen ausgesetzt. Der Zusammenhang der erfassten Messgrößen und ihre Auswirkung auf das Dichtungssystem soll mit Hilfe von Data-Mining-Verfahren untersucht werden.

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  • Optimierung der Produktqualität bei OSRAM Schwabmünchen

    Im Rahmen des Projekts werden Produktionsdaten aus der WolframDrahtproduktion aus dem OSRAM Werk Schwabmünchen auf der SDIL Plattform gespeichert und mithilfe von IBM Tools analysiert. Infolge der Analyse werden Datenmodelle erstellt, die durch bestimmte Prognosen und Regeln die Drahtqualität („Spaltigkeit und Länge) optimieren. Für das Data-Mining wird das Standard-Prozess-Modell CRISP-DM implementiert.

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  • Enhancing Traffic Flow Forecasting with Environmental Models

    The main task of the project is traffic flow forecasting for a region traffic network. In this project, traffic flow forecasting with environmental models is proposed. Nowadays, traffic flow forecasting considers mainly information from one sensor or one specific roadway. However, information from neighbor sensors and other sensors in the traffic subnet could be leveraged in order to improve the state-of-the-art forecasting models.

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  • Optimization of the production processes at John Deere

    The project mainly aims at the reduction of the rework and the avoidance of errors during the production of tractors at the John Deere factory in Mannheim. These two objectives are realized through a data analysis of the error information, the test protocols and their interdependencies. Based on the results of the data analysis, we can make prognoses and rules for the production planning that help the company to take one step further in the process of self-optimization.

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  • All-Time Parts Prediction (ATP) Demo

    ATP predicts the demand for service parts (especially in the automotive industry) for so-called long-time-buy or all-time-buy decisions. This future demand may cover the next 10-20 years and is difficult to estimate, which often leads to buying way too much. Consequently, after many years of sitting in the warehouse, at the end, huge amounts need to be scrapped. This causes high inventory and warehousing costs, which can be significantly reduced by more accurate demand predictions. The IBM ATP Solution has been developed to do exactly that: to predict all-time demand with high accuracy.

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