Dynamic feature evaluation on streaming acoustic emission data for adhesively bonded joints for composite wind turbine blade

Abstract

Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight into different features with damage accumulation. First, the procedure of dynamic feature evaluation is presented based on three basic algorithms. Second, the streaming acoustic emission data are collected from the adhesively bonded composite single-lap joint subjected to quasi-static tensile loads. Third, further efforts are made so as to explore the information contained as well as to interpret the effect of damage accumulation. It is found that different conventional acoustic emission features show distinctive functions, including damage mode identification, damage process indication, and both of them. Informative features for damage pattern recognition are independent on damage accumulation. Useful features for damage process description show sensitive dynamic characteristics with damage accumulation, especially before the complete failure of the specimen. Furthermore, dynamic feature evaluation can be used to detect singular signals.

A new quantitative acoustic emission model for damage characterization of composite laminates using original waveforms

“…Thus, MFCC 0 acts like the AE energy, signal strength and absolute energy, whose roles are to describe the damage process qualitatively. 27 However, MFCC 0 does not show a monotonic tendency.

Figure 4. The distribution of MFCC 0 with the increasing load and cumulative AE energy for specimen (a) ENF1, (b) ENF2, (c) ENF3, and (d) ENF4. Note: ENF: end-notched flexure; MFCC: Mel-frequency cepstral coefficients.

Figure 5. The macroscopic failure diagram of four end-notched flexure specimens.

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“…Thus, MFCC 0 acts like the AE energy, signal strength and absolute energy, whose roles are to describe the damage process qualitatively. 27 However, MFCC 0 does not show a monotonic tendency.…”

“…The time-evolving cumulative AE energy is added here for damage process indication. [27][28][29] In the early stage, the recorded AE signals are comparatively less corresponding to a relatively lower load. Additionally, there is no apparent increase in cumulative AE energy.…”

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…Other parameters are also used with the same goal. See, for instance, Alava and Zapperi 2006 [15] and Xu et al, 2021 [16].…”

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…Other parameters are also used with the same goal. See, for instance, Alava and Zapperi, 2006 [18] and Xu et al, 2021 [19].…”