Characterization of optical ae signals at loading of fibre reinforced composites


Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for biocomposite and glass fiber epoxy (GFE) composites at room temperature and low temperature. For the acquisition of AE signals, fiber optic sensors (FOS) are used that can outperform classical electrical sensors under challenging operational environments. In this paper, we propose the extraction of deep features using different machine learning methods. The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through discriminant analysis, neural networks, and extreme learning machines, combined with feature selection, to estimate the predictive potential of various feature sets. The proposed signal processing structure is focused on the classification of AE signals to recognize the source material, evaluate the predictive importance of extracted features, and evaluate the ability of used FOS for evaluation of material behavior under challenging low-temperature environments.
