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dc.contributor.authorBehúň, Lukáš
dc.contributor.authorSlobodník, Karel
dc.date.accessioned2019-03-04T11:50:41Z-
dc.date.available2019-03-04T11:50:41Z-
dc.date.issued2018
dc.identifier.citationBEHÚŇ, L., SLOBODNÍK, K. Harmonic and pulsed eddy current testing methods as tools for surface and subsurface defect evaluation. Journal of Electrical Engineering = Elektrotechnický časopis, 2018, roč. 69, č. 5, s. 395-399. ISSN 1335-3632.en
dc.identifier.issn1335-3632
dc.identifier.uri2-s2.0-85059583244
dc.identifier.urihttp://hdl.handle.net/11025/31213
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherDe Gruyter Openen
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© De Gruyter Openen
dc.titleHarmonic and pulsed eddy current testing methods as tools for surface and subsurface defect evaluationen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn most publications eddy current testing (ECT) methods are said to be suitable for surface defects up to several millimeters. This paper aims to evaluate the detection capabilities of eddy current testing based on two examples of modern approach. One of them is a harmonic eddy current method with use of wavelet transformation and neural network. The second method is pulsed eddy current testing (PECT) with use of evaluation script based on cross-correlation of measured data with the set of known reference signals. Two samples with five artificial defects each were investigated from the near and far side. Geometric parameters of every defect were then estimated. Results show that both methods are limited by the skin effect. However, the PECT with use of cross-correlation exhibits detection capabilities of subsurface defects in significant depth. On the other hand, harmonic eddy current testing shows lower errors in dimensions estimation for set of surface defects.en
dc.subject.translatedECTen
dc.subject.translatedPECTen
dc.subject.translatededdy currenten
dc.subject.translatedcross-correlationen
dc.subject.translatedneural networken
dc.subject.translatedwavelet transformen
dc.identifier.doi10.2478/jee-2018-0058
dc.type.statusPeer-revieweden
dc.identifier.document-number453413200011
dc.identifier.obd43925299
Appears in Collections:Články / Articles (KTE)
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