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Responding to the Forecast

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2017

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Machines become increasingly complex. At the same time, more and more sensors are installed and information is gathered in order to enable a close to real-time prediction of a machine's state. Compa-nies try to implement Predictive Maintenance strategies to avoid machine downtimes on a large scale. For this purpose, artificial neural networks are applied more and more often. However, the classifica-tion of machine states with artificial neural networks is still not accurate enough. This is partially due to a lack of standards in data processing and in the harmonization of data from different sensor types. We aim to contribute to close these research gaps by developing a standard PM concept for machine and plant manufactures.

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Varwig, Andreas; Kammler, Friedemann; Thomas, Oliver (2017): Responding to the Forecast. INFORMATIK 2017. DOI: 10.18420/in2017_178. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-669-5. pp. 1793-1805. BDSDST 2017 – 3rd International Workshop on Big Data, Smart Data and Semantic Technologies. Chemnitz. 25.-29. September 2017

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