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.

中文翻译：

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使用稀缺的原始结构振动和转移学习对分层进行自主评估

深度学习帮助实现了多种应用的突破；然而，缺乏来自错误状态的数据阻碍了使用深度学习模型开发有效和稳健的诊断策略。这项工作基于稀缺的低频结构振动数据，引入了一种用于层状复合材料分层的自主检测、隔离和量化的迁移学习框架。来自机电耦合模拟模型和层压复合材料试样的实验测试的有限响应数据使用同步提取变换 (SET) 编码为高分辨率时频图像。模拟和实验数据通过基于 AlexNet、GoogleNet、SqueezeNet、ResNet-18、和 VGG-16 来提取低级和高级自主特征。支持向量机 (SVM) 机器学习算法用于评估识别的自主特征如何能够帮助检测、隔离和量化层压复合材料中的分层。使用这些自主特征获得的结果也与使用手工统计特征获得的结果进行了比较。获得的结果令人鼓舞，并提供了一个新的方向，使我们能够在层压复合材料的自主损伤评估方面取得进展，尽管仅限于使用原始稀缺结构振动数据。和量化层压复合材料中的分层。使用这些自主特征获得的结果也与使用手工统计特征获得的结果进行了比较。获得的结果令人鼓舞，并提供了一个新的方向，使我们能够在层压复合材料的自主损伤评估方面取得进展，尽管仅限于使用原始稀缺结构振动数据。和量化层压复合材料中的分层。使用这些自主特征获得的结果也与使用手工统计特征获得的结果进行了比较。获得的结果令人鼓舞，并提供了一个新的方向，使我们能够在层压复合材料的自主损伤评估方面取得进展，尽管仅限于使用原始稀缺结构振动数据。