This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)‐based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large‐scale system. To validate the proposed method, first, a well‐known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pretrained CNNs are fine‐tuned to be adaptable for three‐parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors.

中文翻译：

---

通过深度学习使用极其压缩的数据进行结构健康监控

这项研究介绍了一种基于卷积神经网络（CNN）的新颖方法来进行结构健康监测（SHM），该方法通过基于传输学习（TL）的技术来利用一种形式的压缩响应数据。拟议方法的实施允许在现实的大型系统中进行损害识别和定位。为了验证所提出的方法，首先，对一个著名的基准模型进行了数值模拟。使用加速响应历史以及离散直方图形式的压缩响应数据，可以训练CNN模型，并评估CNN体系结构的鲁棒性。最后，根据响应平均值，标准偏差和比例因子，对经过预训练的CNN进行微调，使其适用于三参数，高度压缩的响应数据。使用训练准确性历史记录，混淆矩阵以及其他性能指标来评估每个CNN实施的性能。除数值研究外，还使用实验振动响应数据进行验证和验证，证明了所提出方法的性能。结果表明，对于具有不同类型传感器的类似结构系统的SHM，深层TL可以有效实施。