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001-es BibID:	BIBFORM053443
Első szerző:	Deák Krisztián (gépészmérnök)
Cím:	Failure diagnostics with SVM in machine maintenance engineering / Krisztián Deák, Imre Kocsis, Attila Vámosi, Zoltán Keviczki
Dátum:	2014
ISSN:	1583-0691
Megjegyzések:	Failure diagnostics as a part of condition monitoring (CM) technique is inevitable in modern industrial practice. Condition Based Maintenance (CBM) identifies all problems that cause further failures and suggests maintenance periods. Reducing maintenance costs and enhancing system availability are largely depends on information provided by precise and accurate failure diagnostics. The approach can be used widely in the several field of the industry. Data acquisition is related to measurement then data processing, feature extraction is needed, finally failure identification. In this paper Support Vector Machine (SVM) is discussed how to be used for diagnosing machines and machine elements. The aim of using SVM is to diagnose the system at a certain moment or predict its actual state in the future. SVM is progressing rapidly several new advances are revealed as the part of machine learning techniques. Due to experiments SVM efficiency could be approximately 90% or even higher.
Tárgyszavak:	Műszaki tudományok Gépészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Analele Universităţii din Oradea. Fascicula Inginerie Managerială şi Tehnologică = Annals of the Oradea University. Fascicle of Management and Technological Engineering. - 23 : 1 (2014), p. 19-24. -
További szerzők:	Kocsis Imre (1969-) (matematikus, gépészmérnök) Vámosi Attila (1979-) (mérnök, informatikus) Keviczki Zoltán
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
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