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http://dx.doi.org/10.18419/opus-10883
Autor(en): | Maschler, Benjamin Ganssloser, Sören Hablizel, Andreas Weyrich, Michael |
Titel: | Deep learning based soft sensors for industrial machinery |
Erscheinungsdatum: | 2020 |
Dokumentart: | Preprint |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-109004 http://elib.uni-stuttgart.de/handle/11682/10900 http://dx.doi.org/10.18419/opus-10883 |
Zusammenfassung: | A multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality. |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Deep Learning Based Soft Sensors for Industrial Machinery.pdf | 359,59 kB | Adobe PDF | Öffnen/Anzeigen |
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