Publication detail

New automatic localization technique of acoustic emission signals in thin metal plates

SEDLÁK, P. HIROSE, Y. KHAN, S. ENOKI, M. ŠIKULA, J.

Original Title

New automatic localization technique of acoustic emission signals in thin metal plates

English Title

New automatic localization technique of acoustic emission signals in thin metal plates

Type

journal article

Language

en

Original Abstract

In acoustic emission (AE) measurement, the information of the arrival time is very important for event location, event identification and source mechanism analysis. Manual picks are time-consuming and sometimes subjective, especially in the case of large volumes of digital data. Various techniques have been presented in the literature and are routinely used in practice such as amplitude threshold, analysis of the long-term average/short-term average (LTA/STA), high-order statistics or artificial neural networks. A new automatic determination technique of the first arrival times of AE signals is presented for thin metal plates. Based on Akaike's information criterion, proposed algorithm of the first arrival detection uses a specific characteristic function, which is sensitive to change of frequency in contrast to others such as envelope of the signal. The approach is applied to data sets of three different tests. Reliable results show the potential of our approach.

English abstract

In acoustic emission (AE) measurement, the information of the arrival time is very important for event location, event identification and source mechanism analysis. Manual picks are time-consuming and sometimes subjective, especially in the case of large volumes of digital data. Various techniques have been presented in the literature and are routinely used in practice such as amplitude threshold, analysis of the long-term average/short-term average (LTA/STA), high-order statistics or artificial neural networks. A new automatic determination technique of the first arrival times of AE signals is presented for thin metal plates. Based on Akaike's information criterion, proposed algorithm of the first arrival detection uses a specific characteristic function, which is sensitive to change of frequency in contrast to others such as envelope of the signal. The approach is applied to data sets of three different tests. Reliable results show the potential of our approach.

Keywords

Acoustic emission; First arrival; Akaike's information criterion; Thin metal plates

RIV year

2009

Released

01.02.2009

Publisher

Elsevier

Location

online doi:10.1016/j.ultras.2008.09.005

Pages from

254

Pages to

262

Pages count

9

URL

BibTex


@article{BUT48947,
  author="Petr {Sedlák} and Yuichiro {Hirose} and Sabrina A. {Khan} and Manabu {Enoki} and Josef {Šikula}",
  title="New automatic localization technique of acoustic emission signals in thin metal plates",
  annote="In acoustic emission (AE) measurement, the information of the arrival time is very important for event location, event identification and source mechanism analysis. Manual picks are time-consuming and sometimes subjective, especially in the case of large volumes of digital data. Various techniques have been presented in the literature and are routinely used in practice such as amplitude threshold, analysis of the long-term average/short-term average (LTA/STA), high-order statistics or artificial neural networks.
A new automatic determination technique of the first arrival times of AE signals is presented for thin metal plates. Based on Akaike's information criterion, proposed algorithm of the first arrival detection uses a specific characteristic function, which is sensitive to change of frequency in contrast to others such as envelope of the signal. The approach is applied to data sets of three different tests. Reliable results show the potential of our approach.",
  address="Elsevier",
  chapter="48947",
  doi="10.1016/j.ultras.2008.09.005",
  institution="Elsevier",
  journal="ULTRASONICS",
  number="2",
  volume="49",
  year="2009",
  month="february",
  pages="254--262",
  publisher="Elsevier",
  type="journal article"
}