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Statements

Subject Item
n2:RIV%2F61388998%3A_____%2F10%3A00363592%21RIV12-AV0-61388998
rdf:type
n5:Vysledek skos:Concept
rdfs:seeAlso
http://www.ndt.net/search/abstract.php3?AbsID=10828
dcterms:description
AE localization procedures using artificial neural networks (ANN) represent extremely effective alternative to classical triangulation methods. Nevertheless, their application always requires full-scale, time consuming ANN training on each specific structure. Disadvantage of particularly trained ANN algorithm is in its non-transferability to any other object. A new ANN-based AE source location approach is proposed in this paper to overcome such limitation. The method replaces standard arrival time differences at the ANN inputs by so called signal arrival time profiles, independent on material and scale changes. The ANN training can be also performed theoretically on geometrical models (i.e. without any experimental errors) and learned ANN is then applied on real structures with different dimensions and materials. Such approach enables considerable extension of ANN application possibilities. The use of new AE source location method is illustrated on experimental data obtained during aircraft structure part testing. AE localization procedures using artificial neural networks (ANN) represent extremely effective alternative to classical triangulation methods. Nevertheless, their application always requires full-scale, time consuming ANN training on each specific structure. Disadvantage of particularly trained ANN algorithm is in its non-transferability to any other object. A new ANN-based AE source location approach is proposed in this paper to overcome such limitation. The method replaces standard arrival time differences at the ANN inputs by so called signal arrival time profiles, independent on material and scale changes. The ANN training can be also performed theoretically on geometrical models (i.e. without any experimental errors) and learned ANN is then applied on real structures with different dimensions and materials. Such approach enables considerable extension of ANN application possibilities. The use of new AE source location method is illustrated on experimental data obtained during aircraft structure part testing.
dcterms:title
Neural network AE source location apart from structure size and material Neural network AE source location apart from structure size and material
skos:prefLabel
Neural network AE source location apart from structure size and material Neural network AE source location apart from structure size and material
skos:notation
RIV/61388998:_____/10:00363592!RIV12-AV0-61388998
n3:aktivita
n15:P n15:Z
n3:aktivity
P(FR-TI1/274), P(GAP104/10/1430), Z(AV0Z20760514)
n3:cisloPeriodika
-
n3:dodaniDat
n12:2012
n3:domaciTvurceVysledku
n9:8402469 n9:6309011 n9:1889702
n3:druhVysledku
n13:J
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
274500
n3:idVysledku
RIV/61388998:_____/10:00363592
n3:jazykVysledku
n11:eng
n3:klicovaSlova
AE source location; artificial neural network; arrival time profiles
n3:klicoveSlovo
n14:arrival%20time%20profiles n14:AE%20source%20location n14:artificial%20neural%20network
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[459451F382BD]
n3:nazevZdroje
Journal of Acoustic Emission
n3:obor
n17:BI
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n8:GAP104%2F10%2F1430 n8:FR-TI1%2F274
n3:rokUplatneniVysledku
n12:2010
n3:svazekPeriodika
28
n3:tvurceVysledku
Blaháček, Michal Převorovský, Zdeněk Chlada, Milan
n3:zamer
n19:AV0Z20760514
s:issn
0730-0050
s:numberOfPages
9