Time Series Analysis of Acoustic Emissions in the Asinelli Tower during Local Seismic Activity

Analysis of critical states based on acoustic emission signals during progressive failure of wood

The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

This work focuses on an experimental and numerical investigation into monitoring damage in a cube-shaped concrete specimen under compression. Experimental monitoring uses acoustic emission (AE) signals acquired by two independent measurement apparatuses, and the same damage process is numerically simulated with the lattice discrete element method (LDEM). The results from the experiment and simulation are then compared in terms of their failure load, final configurations, and the evolution of global parameters based on AE signals, such as the b-value coefficient and the natural time approach. It is concluded that the results from the AE analysis present a significant sensitivity to the characteristics of the acquisition systems. However, natural time methods are more robust for determining such differences, indicating the same general tendency for all three data sets.

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

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. There are two main approaches that 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 that were 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 was also performed to reference the study of the other methods. The results indicate that the proposed methods yield reliable indication of failure in the studied structures.

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...]