Acoustic emission waveforms for damage monitoring in composite materials: Shifting in spectral density, entropy and wavelet packet transform

Abstract

Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to ‘flicker noise’ which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi’s entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, ‘dmey’ wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites.

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

“…Specifically, wavelet ‘dmey’ was used. [ 34 ] Since the sampling frequency was set to 1Mhz, the frequency domain from 0 to 500khz is partitioned into eight equal parts according to the Nyquist sampling theorem, as shown in Table 4. In that case, the range of each frequency band is 65 kHz, narrow enough to distinguish various damage patterns.…”

Cluster analysis on damage pattern recognition in carbon/epoxy composites using acoustic emission wavelet packet

“…33 However, there are some interesting exceptions, wavelet "dmey" has recovered most of the spectral energy in all the selected waveforms, which also can be selected to process AE waveforms. 34…”

Investigation of Interlaminar Shear Properties of CFRP Composites at Elevated Temperatures Using the Lempel-Ziv Complexity of Acoustic Emission Signals

“…The feasibility of choosing different wavelets in this type of waveform analysis can extract accurate time-frequency characteristics using the appropriate waveforms. In the authors' previous studies, a novel method for choosing the appropriate wavelet is developed and discussed [42]. In addition, the procedure for CWT can also be found in the authors' previous research works and other standard research works [42][43][44][45][46].…”

“…In the authors' previous studies, a novel method for choosing the appropriate wavelet is developed and discussed [42]. In addition, the procedure for CWT can also be found in the authors' previous research works and other standard research works [42][43][44][45][46]. To summarise the time-frequency analysis performed in this study, the analytical Morlet wavelet is chosen and used in the CWT.…”