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Statements

Subject Item
n2:RIV%2F61388998%3A_____%2F10%3A00349882%21RIV11-GA0-61388998
rdf:type
skos:Concept n20:Vysledek
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 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 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:_____/10:00349882!RIV11-GA0-61388998
n4:aktivita
n11:Z n11:P
n4:aktivity
P(FR-TI1/274), P(GAP104/10/1430), Z(AV0Z20760514)
n4:dodaniDat
n10:2011
n4:domaciTvurceVysledku
n5:1889702 n5:8402469 n5:6309011
n4:druhVysledku
n18:D
n4:duvernostUdaju
n9:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
274501
n4:idVysledku
RIV/61388998:_____/10:00349882
n4:jazykVysledku
n17:eng
n4:klicovaSlova
AE source location; artificial neural networks; arrival time profiles
n4:klicoveSlovo
n6:arrival%20time%20profiles n6:AE%20source%20location n6:artificial%20neural%20networks
n4:kontrolniKodProRIV
[2316A8C42ACE]
n4:mistoKonaniAkce
Vídeň
n4:mistoVydani
Vienna
n4:nazevZdroje
29th Europen Conference on Acoustic Emission Testing
n4:obor
n19:BI
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n12:FR-TI1%2F274 n12:GAP104%2F10%2F1430
n4:rokUplatneniVysledku
n10:2010
n4:tvurceVysledku
Převorovský, Zdeněk Chlada, Milan Blaháček, Michal
n4:typAkce
n13:EUR
n4:zahajeniAkce
2010-09-08+02:00
n4:zamer
n7:AV0Z20760514
s:numberOfPages
8
n16:hasPublisher
TÚV Austria
n15:isbn
978-3-200-01956-0