Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

Version 1 : Received: 27 November 2021 / Approved: 3 December 2021 / Online: 3 December 2021 (11:37:56 CET)

How to cite: Friedrich, L.F.; Cezar, É.S.; Colpo, A.B.; Tanzi, B.N.R.; Sobczyk, M.; Lacidogna, G.; Niccolini, G.; Kosteski, L.E.; Iturrioz, I. Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals. Preprints 2021, 2021120052 (doi: 10.20944/preprints202112.0052.v1). Friedrich, L.F.; Cezar, É.S.; Colpo, A.B.; Tanzi, B.N.R.; Sobczyk, M.; Lacidogna, G.; Niccolini, G.; Kosteski, L.E.; Iturrioz, I. Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals. Preprints 2021, 2021120052 (doi: 10.20944/preprints202112.0052.v1).

Abstract

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

Keywords

acoustic emission; long-range correlations; natural time analysis; heterogeneous materials

Subject

ENGINEERING, Civil Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.