Abstract

As the temporal and spatial resolution of monitoring data drastically increases by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. One such real-time deep learning platform that was developed to solve damage detection and localisation challenge in the authors previous paper. This network was trained by using simulated structural connection with a variety of loading cases, damage scenarios, and measurement noise levels for robust diagnosis of damage. In this article, this platform is validated by using the data collected by Digital Image Correlation (DIC) which offers a non-contact method to measure full-field strain by increasing the flexibility of their implementation. Nevertheless, the capabilities of DIC while measuring small strain responses is limited. This article first investigates the accuracy of the strain measurements of a structural component subjected to operational loads which are often smaller than 50 $\mu \epsilon .$ The accuracy of three DIC systems with different camera resolutions is compared with the measurements collected by strain gauges and finite element model. Then, the performance and efficiency of damage diagnosis approach is evaluated on two induced damage conditions.

摘要

随着传感技术的进步，监测数据的时间和空间分辨率急剧增加，结构健康监测应用达到了大数据的门槛。深度神经网络非常适合使用大型代表性训练数据集来学习复杂的损伤特征。一个这样的实时深度学习平台，旨在解决作者之前论文中的损伤检测和定位挑战。该网络通过使用模拟结构连接与各种载荷情况、损坏场景和测量噪声水平进行训练，以实现对损坏的稳健诊断。在本文中，该平台通过使用数字图像相关 (DIC) 收集的数据进行验证，DIC 提供了一种非接触式方法，通过增加其实施的灵活性来测量全场应变。然而，DIC 在测量小应变响应时的能力是有限的。本文首先研究了承受操作载荷（通常小于 50）的结构部件的应变测量精度。$\mu \epsilon .$将具有不同相机分辨率的三个 DIC 系统的精度与应变仪和有限元模型收集的测量值进行比较。然后，在两种诱导损伤条件下评估损伤诊断方法的性能和效率。