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dc.contributor.authorKühnert, Christian*
dc.date.accessioned2021-02-11T10:59:12Z
dc.date.available2021-02-11T10:59:12Z
dc.date.issued2013*
dc.date.submitted2019-07-30 20:02:02*
dc.identifier35546*
dc.identifier.issn18636489*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/44557
dc.description.abstractControl systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant's devices the fault can lead to an alarm flood. This again hides the original location of the causing device. In this work several data-driven approaches for root cause localization are proposed, compared and combined. All methods analyze disturbed process data for backtracking the propagation path.*
dc.languageEnglish*
dc.relation.ispartofseriesKarlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.otherTime series*
dc.subject.otherSignal processing*
dc.subject.otherData Mining*
dc.subject.otherSystem identification*
dc.subject.otherCausality*
dc.titleData-driven Methods for Fault Localization in Process Technology*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000036427*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731500988*
oapen.pagesXVIII, 194 p.*
oapen.volume15*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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