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In der neuen Expertise des Forschungsbeirats Industrie 4.0 untersuchen das FIR e. V. an der RWTH Aachen und das Industrie 4.0 Maturity Center den Status-quo und die aktuellen Herausforderungen der deutschen Industrie bei der Nutzung und wirtschaftlichen Verwertung von industriellen Daten. Handlungsoptionen für Unternehmen, Verbände, Politik und Wissenschaft zeigen auf, wie der Nutzungsgrad der Datenbasis erhöht werden kann und wie sich Potenziale bei der Monetarisierung ausschöpfen lassen. Der Fokus liegt dabei auf produzierenden Unternehmen.
In Germany’s transition to a more sustainable industrial landscape, electricity generated by wind turbines (WT) remains a mainstay of the energy mix. Operating and maintenance costs, which account for roughly 25% of electricity generation costs in onshore WTs make improvements of maintenance activities a key lever in the economic operation of WTs. Prescriptive maintenance is a possible approach for improved maintenance activities. It is a concept where asset condition data is used to recommend specific actions and has great potential for the operation of wind parks. However, especially small, but also large wind park operators, and maintenance service providers often struggle with the implementation of such a new maintenance approach. As a part of the research project ReStroK, a learning game has been developed to support the training and familiarization of maintenance technicians with the concepts and underlying principles of this maintenance approach. In this paper, the concept for the development of a learning game will be presented. Multiple scenarios for its usage and their corresponding requirements will be discussed and an overview over the game will be given.