Full metadata record
DC FieldValueLanguage
dc.contributor.authorGoren Huber, Lilach-
dc.descriptionBest Presentation Awardde_CH
dc.description.abstractDeveloping predictive maintenance algorithms for industrial systems is a growing trend in numerous application fields. Whereas applied research methods have been rapidly advancing, implementations in commercial systems are still lagging behind. One of the main reasons for this delay is the fact that most methodological advances have been focusing on development of data driven algorithms for fault detection, diagnosis or prognosis, ignoring some of the crucial aspects that are required for scaling these algorithms to large fleets of multi-component heterogeneous machines under varying operating conditions, and making sure that their implementation is technically feasible. In this talk I summarize results of an extensive project, developing a deep learning based fault detection scheme for wind farms. I emphasize the elements of this scheme that enabled scaling it up for commercial implementation which took place recently.de_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectTransfer learningde_CH
dc.subjectFleet PHMde_CH
dc.subjectCondition based maintenancede_CH
dc.subjectMulti-component systemde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titleScaling-up deep learning based predictive maintenance for commercial machine fleets : a case studyde_CH
dc.typeKonferenz: Sonstigesde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.conference.details9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22.-23. Juni 2022de_CH
zhaw.publication.reviewNot specifiedde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.