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Acoustic emission localization on ship hull structures using a deep learning approach
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Department of Technology and Innovation (ITI), University of Southern Denmark (SDU).
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
Number of Authors: 32016 (English)In: Vibroengineering Procedia, ISSN 2345-0533, Vol. 9, p. 56-61Article in journal (Refereed) Published
Abstract [en]

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor

Place, publisher, year, edition, pages
2016. Vol. 9, p. 56-61
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-61252Scopus ID: 2-s2.0-85002342355OAI: oai:DiVA.org:ltu-61252DiVA, id: diva2:1059805
Conference
23rd International Conference on Vibroengineering, Istanbul, Turkey, 7-9 October 2016
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 636834
Note

2016-12-23 (andbra);Konferensartikel i tidskrift

Available from: 2016-12-23 Created: 2016-12-23 Last updated: 2018-05-29Bibliographically approved

Open Access in DiVA

fulltext(148 kB)2 downloads
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Other links

Scopushttps://www.jvejournals.com/article/17769

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Georgoulas, GeorgiosNikolakopoulos, George

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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