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dc.contributor.advisorvon Luck, Kai-
dc.contributor.authorAssendorp, Jan Paul
dc.date.accessioned2020-09-29T14:25:46Z-
dc.date.available2020-09-29T14:25:46Z-
dc.date.created2017
dc.date.issued2017-11-22
dc.identifier.urihttp://hdl.handle.net/20.500.12738/8162-
dc.description.abstractDas Erkennen von Anomalien in Sensordaten ist ein wichtiger Anwendungsfall in der Industrie, um Fehler in maschinellen Prozessen frühzeitig erkennen zu können und potentiellen Schäden vorzubeugen. In dieser Arbeit wird ein Deep-Learning-Verfahren entwickelt, welches in mehrdimensionalen Sensordaten ungewöhnliche Muster erkennen kann. Dafür werden Echtdaten aus einer industriellen Anwendung verwendet.de
dc.description.abstractAnomaly detection is crucial for the procactive detection of fatal failures of machines in industry applications. This thesis implements a deep learning algorithm for the task of anomaly detection in multivariate sensor data. The dataset is taken from a real-world application.en
dc.language.isodede
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/-
dc.subjectDeep-Learningde
dc.subjectMachine-Learningde
dc.subject.ddc004 Informatik
dc.titleDeep learning for anomaly detection in multivariate time series datade
dc.typeThesis
openaire.rightsinfo:eu-repo/semantics/openAccess
thesis.grantor.departmentDepartment Informatik
thesis.grantor.placeHamburg
thesis.grantor.universityOrInstitutionHochschule für angewandte Wissenschaften Hamburg
tuhh.contributor.refereeMeisel, Andreas-
tuhh.gvk.ppn1005248966
tuhh.identifier.urnurn:nbn:de:gbv:18302-reposit-81644-
tuhh.note.externpubl-mit-pod
tuhh.note.intern1
tuhh.oai.showtrueen_US
tuhh.opus.id4110
tuhh.publication.instituteDepartment Informatik
tuhh.type.opusMasterarbeit-
dc.subject.gndAnomalieerkennung
dc.type.casraiSupervised Student Publication-
dc.type.dinimasterThesis-
dc.type.drivermasterThesis-
dc.type.statusinfo:eu-repo/semantics/publishedVersion
dc.type.thesismasterThesis
dcterms.DCMITypeText-
tuhh.dnb.statusdomain-
item.creatorGNDAssendorp, Jan Paul-
item.fulltextWith Fulltext-
item.creatorOrcidAssendorp, Jan Paul-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.advisorGNDvon Luck, Kai-
item.languageiso639-1de-
item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
item.openairetypeThesis-
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