Experimental and Computational Analysis of Acoustic Emission Waveforms for SHM Applications

Abstract

Acoustic emission (AE) waveform monitoring has a great potential to use as an SHM technology. The design of an SHM system requires a thorough understanding not only in the AE hit level but also in the waveform level. Capturing the fatigue-crack generated acoustic waves from a thin aircraft material is a challenging task. The present work focuses on capturing the AE waveform from the fatigue-crack of the thin specimen. The capability of the piezoelectric wafer active sensor (PWAS), commonly used for SHM design, in capturing the fatigue-crack AE has been illustrated. The AE waveforms were captured from an in-situ AE-fatigue experiment. The AE waveforms were analyzed and two distinct types of AE waveforms were frequently observed. Multiphysics FEM simulations also showed the two types of AE waveforms corresponding to the two types of AE events. The local interaction of the AE waveform with the fatigue-crack was also observed that was yet to capture experimentally. The concept of seismological moment tensor seemed to work to simulate the AE source in the thin structure. It has been shown that different components of moment tensor can successfully simulate the different groups of experimental AE waveforms.

Compatibility and challenges in machine learning approach for structural crack assessment

“…29 Since early studies in the SHMA area, efforts have been focused primarily on the correlation of AE signals to various kinds of crack mechanisms due to it can be applied in-situ and has a high sensitivity to the initial, local damages. 30 For example, the relationship between the frequency content of AEs and fracture mechanisms. 31 An assessment of various clustering algorithms for AE using traditional AE parameters such as functions implementing a self-organising neural network, the K-nearest-neighbour classifier and the k-means method was performed.…”

“…Overall, these studies 30,[117][118][119][120][121][122][123][124] highlight the need for combining ML models with the physics-based model in different SHMA applications to interpret the physical meaning of the model parameters and explaining the effects of the feature on the accuracy of the ML model. However, combining physics-based models and machine learning continues a challenging problem particularly for the community of structural crack performance assessment and will remain to be investigated in future research.…”

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

“…Zhang et al [ 7 ] studied the acoustic emission signatures of fatigue damages in an idealized bevel gear spline and identified two different AE signal signatures for plastic deformation and a crack jump. Bhuiyan et al [ 8 , 9 , 10 ] studied the AE signal signatures recorded by PWAS transducers during a fatigue-crack growth experiment in thin metallic plates. In this research, under a slow frequency of fatigue loading (<0.25 Hz), the AE signals were recorded for a short advancement of crack length, and eight signal signatures were identified related to crack growth, and crack rubbing and clapping.…”

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals

“…The acoustic emission signatures of fatigue damages in an idealized bevel gear spline and two different AE signal signatures for plastic deformation and crack jump were identified by Zhang et al [ 39 ]. The AE signal signatures recorded by piezoelectric wafer active sensor (PWAS) transducers during a fatigue crack growth event in thin metallic plates were studied by Bhuiyan et al [ 40 , 41 , 42 ]. In this research, under a slow frequency of fatigue loading (<0.25 Hz), for a short advancement of crack length, the AE signals were recorded, and eight signal signatures were discovered related to crack growth and crack rubbing and clapping.…”