Responding to the Forecast
| dc.contributor.author | Varwig, Andreas | |
| dc.contributor.author | Kammler, Friedemann | |
| dc.contributor.author | Thomas, Oliver | |
| dc.contributor.editor | Eibl, Maximilian | |
| dc.contributor.editor | Gaedke, Martin | |
| dc.date.accessioned | 2017-08-28T23:47:17Z | |
| dc.date.available | 2017-08-28T23:47:17Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.18420/in2017_178 | |
| dc.identifier.isbn | 978-3-88579-669-5 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2017 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-275 | |
| dc.subject | Predictive Maintenance | |
| dc.subject | Big Data Analytics | |
| dc.subject | Sensor Data Processing | |
| dc.subject | Neural Networks | |
| dc.subject | Automated Diagnostics | |
| dc.subject | Decision Support Systems | |
| dc.title | Responding to the Forecast | en |
| dc.title.subtitle | Towards the Integration of Machine State Prediction and Required Maintenance Services | en |
| gi.citation.endPage | 1805 | |
| gi.citation.startPage | 1793 | |
| gi.conference.date | 25.-29. September 2017 | |
| gi.conference.location | Chemnitz | |
| gi.conference.sessiontitle | BDSDST 2017 – 3rd International Workshop on Big Data, Smart Data and Semantic Technologies |
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