Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel
Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel
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DOI：
10.1016/j.compositesb.2021.109450
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发表时间：
2021-10-30
影响因子：
13.1
通讯作者：
Kundu, Abhishek
中科院分区：
文献类型：
--
作者：
Sikdar, Shirsendu;Liu, Dianzi;Kundu, Abhishek
本文采用数据驱动的深度学习方法研究了轻型复杂复合材料结构的结构健康监测，以促进自动学习转换后的信号特征到损伤类别的映射。为此，使用压电声发射传感器网络对复合材料样品进行了一系列基于声发射的实验室实验。利用连续小波变换对复合板上的传感器网络采集的声发射时域信号进行处理，提取时频尺度图。提出了一种基于卷积神经网络的深度学习架构，用于自动从尺度图图像中提取离散损伤特征，并使用它们对复合材料面板中的损伤源区域进行分类。所提出的深度学习方法已经显示出有效的损伤监测潜力，对于看不见的数据集以及全新的相邻损伤数据集具有高训练、验证和测试精度。此外，所提出的网络进行训练，验证和测试，只有从原始AE数据中提取的峰值信号数据。峰值信号尺度图数据的应用表明，在高训练，验证和测试精度的损伤源分类性能的显着改善。
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.