Publication:

A semi-supervised anomaly detection approach for detecting mechanical failures

Date

2024
Journal article
https://doi.org/10.1017/S0890060424000131
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dc.contributor.authorSoete, Colin
dc.contributor.authorRademaker, Michael
dc.contributor.authorVan Hoecke, Sofie
dc.contributor.imecauthorSoete, Colin
dc.contributor.imecauthorRademaker, Michael
dc.contributor.imecauthorVan Hoecke, Sofie
dc.contributor.orcidimecSoete, Colin::0000-0002-2035-3466
dc.contributor.orcidimecRademaker, Michael::0000-0001-7124-692X
dc.contributor.orcidimecVan Hoecke, Sofie::0000-0002-7865-6793
dc.date.accessioned2024-11-22T11:02:03Z
dc.date.available2024-11-21T16:53:53Z
dc.date.available2024-11-22T11:02:03Z
dc.date.issued2024
dc.identifier.doi10.1017/S0890060424000131
dc.identifier.issn0890-0604
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/44814
dc.publisherCAMBRIDGE UNIV PRESS
dc.source.beginpagee16
dc.source.endpage12
dc.source.issue/
dc.source.journalAI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING
dc.source.numberofpages12
dc.source.volume38
dc.title

A semi-supervised anomaly detection approach for detecting mechanical failures

dc.typeJournal article
dspace.entity.typePublication
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