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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F04%3A03099698%21RIV%2F2005%2FMSM%2F212305%2FN
rdf:type
n6:Vysledek skos:Concept
dcterms:description
This paper addresses the problem of on-line diagnosis of cavitation in centrifugal pumps. The paper introduces an application of the Open Prediction System (OPS) to cavitation diagnosis. The application of OPS results in an algorithmic framework for diagnosis of cavitation in centrifugal pumps. The diagnosis is based on repeated evaluation of a data scan providing full record of input signals which are observed for a fixed short period of time. Experimental verification of the algorithmic framework and the proposed methodology proved that a condition monitoring system built upon them is capable of diagnosing a wide range of cavitation conditions that can occur in a centrifugal pump, including the very early incipient cavitation. This paper addresses the problem of on-line diagnosis of cavitation in centrifugal pumps. The paper introduces an application of the Open Prediction System (OPS) to cavitation diagnosis. The application of OPS results in an algorithmic framework for diagnosis of cavitation in centrifugal pumps. The diagnosis is based on repeated evaluation of a data scan providing full record of input signals which are observed for a fixed short period of time. Experimental verification of the algorithmic framework and the proposed methodology proved that a condition monitoring system built upon them is capable of diagnosing a wide range of cavitation conditions that can occur in a centrifugal pump, including the very early incipient cavitation. Není k dispozici
dcterms:title
Není k dispozici Intelligent Diagnosis and Learning in Centrifugal Pumps Intelligent Diagnosis and Learning in Centrifugal Pumps
skos:prefLabel
Intelligent Diagnosis and Learning in Centrifugal Pumps Intelligent Diagnosis and Learning in Centrifugal Pumps Není k dispozici
skos:notation
RIV/68407700:21230/04:03099698!RIV/2005/MSM/212305/N
n3:strany
513 ; 522
n3:aktivita
n13:Z
n3:aktivity
Z(MSM 212300013)
n3:dodaniDat
n9:2005
n3:domaciTvurceVysledku
n15:3260208 n15:7803664 n15:5879523 n15:5112605
n3:druhVysledku
n4:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
568501
n3:idVysledku
RIV/68407700:21230/04:03099698
n3:jazykVysledku
n8:eng
n3:klicovaSlova
Industrial diagnosis; cavitation; condition-based maintenance; data mining; mining dependent data
n3:klicoveSlovo
n17:cavitation n17:condition-based%20maintenance n17:data%20mining n17:mining%20dependent%20data n17:Industrial%20diagnosis
n3:kontrolniKodProRIV
[6D66C5AAB64A]
n3:mistoKonaniAkce
Vídeň
n3:mistoVydani
New York
n3:nazevZdroje
Emerging Solutions for Future Manufacturing Systems
n3:obor
n10:JC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n9:2004
n3:tvurceVysledku
Flek, Ondřej Nováková, Lenka Kléma, Jiří Kout, Jan
n3:typAkce
n21:WRD
n3:zahajeniAkce
2004-09-27+02:00
n3:zamer
n11:MSM%20212300013
s:numberOfPages
10
n12:hasPublisher
Springer-Verlag
n16:isbn
0-387-22828-4
n5:organizacniJednotka
21230