Gast ::
000861032 001__ 861032 000861032 005__ 20240619082519.0 000861032 037__ $$aRWTH-2022-11586 000861032 041__ $$aEnglish 000861032 1001_ $$0P:(DE-82)IDM05168$$aBrumand-Poor, Faras$$b0$$eCorresponding author$$urwth 000861032 1112_ $$aGlobal Fluid Power Symposium$$cNeapel$$d2022-10-12 - 2022-10-14$$gGFPS$$wGermany 000861032 245__ $$aImplementation of Variational Autoencoder for Dimension Reduction of a Hydraulic System$$honline 000861032 260__ $$c2022 000861032 29510 $$a[Global Fluid Power Symposium, GFPS, 2022-10-12 - 2022-10-14, Neapel, Germany] 000861032 3367_ $$033$$2EndNote$$aConference Paper 000861032 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1671441116_31138 000861032 3367_ $$2BibTeX$$aINPROCEEDINGS 000861032 3367_ $$2DRIVER$$aconferenceObject 000861032 3367_ $$2DataCite$$aOutput Types/Conference Paper 000861032 3367_ $$2ORCID$$aCONFERENCE_PAPER 000861032 653_7 $$aDigitalisierung; Data-based Condition Monitoring; Deep Unsupervised Learning; Variational Autoencoder; Dimension Reduction; Long Short-Term Memory; Neuronale Netze 000861032 7001_ $$0P:(DE-82)IDM04695$$aMakansi, Faried$$b1$$urwth 000861032 7001_ $$0P:(DE-82)IDM02480$$aSchmitz, Katharina$$b2$$urwth 000861032 7001_ $$0P:(DE-HGF)0$$aJiakun, Liao$$b3 000861032 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM05168$$aRWTH Aachen$$b0$$kRWTH 000861032 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM04695$$aRWTH Aachen$$b1$$kRWTH 000861032 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM02480$$aRWTH Aachen$$b2$$kRWTH 000861032 9151_ $$0StatID:(DE-HGF)0031$$2StatID$$aPeer reviewed article$$x0 000861032 9201_ $$0I:(DE-82)412810_20180620$$k412810$$lLehrstuhl und Institut für fluidtechnische Antriebe und Systeme$$x0 000861032 961__ $$c2022-12-19T15:08:15.383875$$x2022-12-19T15:08:15.383875$$z2022-12-20 000861032 980__ $$aI:(DE-82)412810_20180620 000861032 980__ $$aUNRESTRICTED 000861032 980__ $$aVDBINPRINT 000861032 980__ $$acontrib