Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Abstract

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.

A Comparison of Two Types of Acoustic Emission Sensors for the Characterization of Hydrogen-Induced Cracking

“…Therefore, the Akaike Information Criterion (AIC) [ 28 ] was applied to determine the signal arrival time. Based on the autoregressive process (AP), the AIC picker was used to identify the onset point by finding the separation point of noise and signal through the minimized AIC value [ 29 ]. The arrival time picked by the AIC method is often more accurate than that determined by the threshold-crossing method.…”

Rock Crack Initiation Triggered by Energy Digestion

“…1. However, it is di cult to strictly correspond to the microscopic damage and reveal the process of damage evolution with non-destructive [14,15]and have not yet been universally formed for the most commonly used Prediction evaluation criteria method [16]. More recently, the perspective of energy evolution has provided a new idea for the study of crack evolution.…”

ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location

“…This area indicates events in different acoustic scenes, especially in spatial room impulse responses (SRIRs), realistic spatialization, and reverberation environments. The task of sound event location can be applied to many areas such as machine automatic driver [1][2][3][4], smart automation [5][6][7][8], ocean positioning technology [9][10][11], quality monitoring [12], medical supervision [13], navigation without visual input or with occluded targets [14], selflocalization [15], smart-phone audio assistant [16], acoustic device monitoring [17], scene visualization systems [18], moving source tracking [19], virtual reality [20] and etc. The processing flow and applications can be shown in Fig.…”