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

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.

Detecting Criticality by Exploring the Acoustic Activity in Terms of the “Natural-Time” Concept

“…In this context, the Natural Time concept became gradually a valuable alternative tool in the tedious effort of the scientific community to develop theories for the prediction of upcoming catastrophic events. Soon after the concept was introduced it was adopted for data analysis in an amazingly wide range of scientific disciplines, from seismology [26][27][28][29][30], to medicine [31,32], condensed matter physics [33] and mechanics of materials [34][35][36][37][38][39][40].…”