Acoustic emission localization on ship hull structures using a deep learning approach

George Georgoulas, Vassilios Kappatos, George Nikolakopoulos

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In 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.
Original languageEnglish
Title of host publicationVibroengineering PROCEDIA
PublisherJVE International Ltd.
Publication date2016
Pages56‑61
Publication statusPublished - 2016
Event23rd International Conference of Vibroengineering on Modeling, Identification and Fault Detection in Oil and Gas Equipment and Infrastructures - Istanbul, Turkey
Duration: 7. Oct 20169. Oct 2016
Conference number: 23
http://www.jveconferences.com/about-istanbul-conference

Conference

Conference23rd International Conference of Vibroengineering on Modeling, Identification and Fault Detection in Oil and Gas Equipment and Infrastructures
Number23
CountryTurkey
CityIstanbul
Period07/10/201609/10/2016
Internet address
SeriesVibroengineering Procedia
Volume9
ISSN2345-0533

Keywords

  • Acoustic emission
  • Deep belief networks
  • Deep learning
  • Ship hull
  • Source localizations

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