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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.
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.
Number of Authors: 3
2016 (English)In: Vibroengineering Procedia, ISSN 2345-0533, Vol. 9, 56-61 p.Article 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, 56-61 p.
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-61252ScopusID: 2-s2.0-85002342355OAI: oai:DiVA.org:ltu-61252DiVA: diva2:1059805
Conference
23rd International Conference on Vibroengineering, Istanbul, Turkey, 7-9 October 2016
Note

Konferensartikel i tidskrift 2016-12-23 (andbra)

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

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