Damage mode identification of adhesive composite joints under hygrothermal environment using acoustic emission and machine learning

2019 ◽  
Vol 211 ◽  
pp. 351-363 ◽  
Author(s):  
D. Xu ◽  
P.F. Liu ◽  
J.G. Li ◽  
Z.P. Chen
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Composite Joints ◽  
Damage Mode ◽  
Mode Identification ◽  
Hygrothermal Environment

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

2021 ◽  
pp. 147592172110017
Author(s):  
Dong Xu ◽  
Pengfei Liu ◽  
Zhiping Chen ◽  
Qimao Cai ◽  
Jianxing Leng
Keyword(s):  
Acoustic Emission ◽  
Damage Accumulation ◽  
Dynamic Feature ◽  
Damage Mode ◽  
Adhesively Bonded ◽  
Emission Data ◽  
Premature Failure ◽  
Mode Identification ◽  
Feature Evaluation ◽  
Damage Process

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.

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