Challenges and Limitations in the Identification of Acoustic Emission Signature of Damage Mechanisms in Composites Materials

Abstract

Acoustic emission is a part of structural health monitoring (SHM) and prognostic health management (PHM). This approach is mainly based on the activity rate and acoustic emission (AE) features, which are sensitive to the severity of the damage mechanism. A major issue in the use of AE technique is to associate each AE signal with a specific damage mechanism. This approach often uses classification algorithms to gather signals into classes as a function of parameters values measured on the signals. Each class is then linked to a specific damage mechanism. Nevertheless, each recorded signal depends on the source mechanism features but the stress waves resulting from the microstructural changes depend on the propagation and acquisition (attenuation, damping, surface interactions, sensor characteristics and coupling). There is no universal classification between several damage mechanisms. The aim of this study is the assessment of the influence of the type of sensors and of the propagation distance on the waveforms parameters and on signals clustering.

Data Merging of AE Sensors with Different Frequency Resolution for the Detection and Identification of Damage in Oxide-Based Ceramic Matrix Composites

“…Most of the time, acoustic emission is collected from resonant sensors due to their good sensibility. However, waveform characterization is strongly dependent on sensor features [ 17 , 18 , 19 ]. Therefore, the effects of sensors’ types are still a challenge.…”

“…It can only obtain partial information and therefore cannot get accurate information about the damage. Several studies have shown that using one type of sensor has many limitations in identifying acoustic signatures [ 17 , 18 , 19 ]. This emphasizes the interest of using multiple data sources and data fusion.…”

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

“…From the contemporary research on delamination in laminated composites, it can be found that most of the research efforts are dedicated to the use of higher-frequency guided waves (e.g., Lamb waves), acoustic emission/acoustic ultrasonic, and mode shape curvatures for the detection, quantification, and localization of delamination [43][44][45][46][47][48][49]. This paper proposes a deep learning framework for the assessment of delamination in piezo-bonded laminated composites using low-frequency structural vibration responses.…”

Dimension Effects on the Acoustic Behavior of TRC Plates

“…Therefore, the shift illustrated in Figure 1b, may be a result of the propagation conditions that mask the characteristics of the source. Indeed, the propagation distance has been shown to be a crucial factor influencing the classification [14,22,23] because of the more effective attenuation of the higher frequencies as well as the "spreading" of the waveforms in the time domain. Apart from experimental AE studies, there are several numerical works on the subject of wave propagation in plates, some examples are shown in [24][25][26].…”