Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10883
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dc.contributorAVAT Automation GmbHde
dc.contributor.authorMaschler, Benjamin-
dc.contributor.authorGanssloser, Sören-
dc.contributor.authorHablizel, Andreas-
dc.contributor.authorWeyrich, Michael-
dc.date.accessioned2020-06-08T09:57:56Z-
dc.date.available2020-06-08T09:57:56Z-
dc.date.issued2020de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/10900-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-109004de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-10883-
dc.description.abstractA 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.en
dc.language.isoende
dc.relation.uridoi:10.13140/RG.2.2.24401.56167/1de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc004de
dc.subject.ddc600de
dc.subject.ddc620de
dc.subject.ddc621.3de
dc.subject.ddc670de
dc.titleDeep learning based soft sensors for industrial machineryen
dc.typepreprintde
ubs.fakultaetInformatik, Elektrotechnik und Informationstechnikde
ubs.fakultaetFakultätsübergreifend / Sonstige Einrichtungde
ubs.institutInstitut für Automatisierungstechnik und Softwaresystemede
ubs.institutFakultätsübergreifend / Sonstige Einrichtungde
ubs.publikation.noppnyesde
ubs.publikation.typPreprintde
Appears in Collections:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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