Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10883
Authors: Maschler, Benjamin
Ganssloser, Sören
Hablizel, Andreas
Weyrich, Michael
Title: Deep learning based soft sensors for industrial machinery
Issue Date: 2020
metadata.ubs.publikation.typ: Preprint
URI: http://elib.uni-stuttgart.de/handle/11682/10900
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-109004
http://dx.doi.org/10.18419/opus-10883
Abstract: 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.
Appears in Collections:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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