Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1 Gil, Jung-gu, Seoul 04620, Korea.
Department of Mechanical Design Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.
Sensors (Basel). 2021 Sep 17;21(18):6239. doi: 10.3390/s21186239.
Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.
深度学习在各种应用中取得了突破；然而，由于错误状态下的数据缺乏，使用深度学习模型开发有效和稳健的诊断策略受到了阻碍。本工作提出了一种基于稀缺低频结构振动数据的层合复合材料分层自动检测、隔离和定量的迁移学习框架。使用 SynchroExtracting Transforms (SET) 将机电耦合仿真模型和层合复合材料试件的实验测试的有限响应数据编码成高分辨率时频图像。对模拟和实验数据进行处理，通过基于 AlexNet、GoogleNet、SqueezeNet、ResNet-18 和 VGG-16 的预训练深度学习模型的不同层，提取低水平和高水平的自主特征。支持向量机（SVM）机器学习算法用于评估所识别的自主特征在分层复合材料的检测、隔离和定量中的辅助能力。还将使用这些自主特征获得的结果与使用手工制作的统计特征获得的结果进行了比较。所获得的结果令人鼓舞，并提供了一个新的方向，即使仅限于使用原始稀缺的结构振动数据，也可以允许我们在层合复合材料的自主损伤评估方面取得进展。
