Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel

深度学习 人工智能 卷积神经网络 声发射 信号(编程语言) 计算机科学 小波变换 试验数据 小波 模式识别(心理学) 人工神经网络 材料科学 复合材料 程序设计语言
作者
Shirsendu Sikdar,Dianzi Liu,Abhishek Kundu
出处
期刊:Composites Part B-engineering [Elsevier BV]
卷期号:228: 109450-109450 被引量:93
标识
DOI:10.1016/j.compositesb.2021.109450
摘要

Structural health monitoring for lightweight complex composite structures is being investigated in this paper with a data-driven deep learning approach to facilitate automated learning of the map of transformed signal features to damage classes. Towards this, a series of acoustic emission (AE) based laboratory experiments have been carried out on a composite sample using a piezoelectric AE sensor network. The registered time-domain AE signals from the assigned sensor networks on the composite panel are processed with the continuous wavelet transform to extract time-frequency scalograms. A convolutional neural network based deep learning architecture is proposed to automatically extract the discrete damage features from the scalogram images and use them to classify damage-source regions in the composite panel. The proposed deep-learning approach has shown an effective damage monitoring potential with high training, validation and test accuracy for unseen datasets as well as for entirely new neighboring damage datasets. Further, the proposed network is trained, validated and tested only for the peak-signal data extracted from the raw AE data. The application of peak-signal scalogram data has shown a significant improvement in damage-source classification performance with high training, validation and test accuracy.
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