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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.

This work dives into the spectral realm of acoustic emission waveforms. The acoustic emission waveforms carry a footprint of source, its mechanism, and the information of the medium through which it travels. The idiosyncrasies of these waveforms cannot be visualized from the time-domain parameters. The complex fracture process of the heterogeneous composite, such as concrete, reflects in the spectral disorder of acoustic emission signals. The use of wavelet entropy is proposed to estimate the spectral disorder. To evaluate wavelet entropy, the relative energy distribution in frequency sub-bands is determined using the wavelet transform. The Shannon entropy formulation as a wavelet entropy is utilized for discriminating spatiotemporally distributed acoustic emission events according to their respective level of disorder. The possible twofold application of the wavelet entropy as a signal discriminator and a damage index is qualitatively demonstrated. The increase in the statistical variance of wavelet entropy distribution with the increase in stress level reveals the presence of multi-sources as well as multi-mechanistic fracture process.

This article focuses on the rail crack monitoring using acoustic emission technique in the field typically with complex cracking conditions and high operational noise. A novel crack monitoring strategy based on Tsallis synchrosqueezed wavelet entropy was developed, where synchrosqueezed wavelet transform was introduced to explore the time–frequency characteristics of acoustic emission signals and Tsallis entropy was adopted to quantify the local variation of acoustic emission wavelet coefficients more accurately. The mother wavelet of synchrosqueezed wavelet transform and three key parameters of time-Tsallis synchrosqueezed wavelet entropy, including characteristic frequency band, non-extensive parameter, and time window length, were appropriately determined. The performance of the strategy was validated through field tests with an incipient rail crack and trains running at operating speeds. Time-Tsallis synchrosqueezed wavelet entropy efficiently detected and located the crack by extracting the crack-related transients in acoustic emission signals that were easily submerged in the operational noise. Synchrosqueezed wavelet transform further helped to analyze the mechanisms of these crack-related transients, which were distinguished to be either crack propagation or impact. The experimental results demonstrated that the crack monitoring strategy proposed is able to detect both surface and internal rail cracks even in the noisy environment, highlighting its potential for field applications.