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Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Sensors (Basel, Switzerland), 2023-01, Vol.23 (2), p.693 [Peer Reviewed Journal]

2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23020693 ;PMID: 36679490

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  • Title:
    Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization
  • Author: Melchiorre, Jonathan ; Manuello Bertetto, Amedeo ; Rosso, Marco Martino ; Marano, Giuseppe Carlo
  • Subjects: Accuracy ; acoustic emission ; Acoustic emission testing ; Acoustics ; Akaike Information Criterion (AIC) ; Artificial Intelligence ; artificial neural network ; crack location ; Damage localization ; Datasets ; Earthquake damage ; Earthquakes ; Emission analysis ; Fractals ; Identification ; Identification methods ; Localization ; Methods ; Monitoring ; Neural networks ; Neural Networks, Computer ; Nondestructive testing ; P waves ; seismic signals ; Sensors ; Sound ; source location ; Time series ; Ultrasonics ; Waveforms
  • Is Part Of: Sensors (Basel, Switzerland), 2023-01, Vol.23 (2), p.693
  • Description: The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation's abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23020693
    PMID: 36679490
  • Source: GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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