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dc.contributor.authorThunem, Harald P-J
dc.date.accessioned2019-06-21T13:07:07Z
dc.date.available2019-06-21T13:07:07Z
dc.date.created2019-06-20T14:46:49Z
dc.date.issued2006
dc.identifier.isbn82-7017-568-4
dc.identifier.urihttp://hdl.handle.net/11250/2601719
dc.description.abstractThis paper describes a sub-activity within the CORD Production Separator project focusing on applying neural network-based signal validation and diagnosis tools for abnormality detection and condition assessment of the internal equipment of production separators in the petroleum industry. The tools have been successfully applied for the monitoring of various processes within the nuclear industry, and perform their functions by learning how various process signals are correlated. PEANO will validate and calibrate a sensor by determining any deviations from the expected correlations to other sensors, while ALADDIN recognizes a fault by its associated time-based “signature” in the combined data matrix.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitutt for energiteknikknb_NO
dc.relation.ispartofIFE/HR
dc.relation.ispartofseriesIFE/HR;E-2006/007
dc.titleCondition Assessment of Production Separators using Neuro-Fuzzy Tools for Signal Validation and Diagnosisnb_NO
dc.typeResearch reportnb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© IFE – Institutt for energiteknikk. The publication may be freely cited where the source is acknowledged.nb_NO
dc.source.issueIFE/HR/E-2006/007nb_NO
dc.identifier.cristin1706514
cristin.unitcode7492,5,6,0
cristin.unitnameIntelligente systemer
cristin.ispublishedtrue
cristin.fulltextoriginal


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