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.
A stochastic model for soft tissue failure using acoustic emission data
