论文标题
模型订购方法，用于对具有局部操作激发的大型部署结构的结构健康监测
Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations
论文作者
论文摘要
我们为具有局部操作激发的大型部署结构提供了基于仿真的分类方法。该方法扩展了两级端口降低的减少基本组件（PR-RBC）技术，以提供更快的溶液估计，并以移动载荷的时间域弹性动力学的双曲偏微分方程。建立基于时间域相关功能的功能，以训练分类器，例如人工神经网络并执行损伤检测。该方法在一个带有移动车辆（扮演数字双胞胎的角色）的桥梁示例上进行了测试，以检测裂缝的存在。此类问题具有$ 45 $的参数，并在操作激发，其他滋扰参数和添加的噪声的背景下显示了两级PR-RBC方法和基于相关功能的功能的优点。分类任务的质量通过足够大的合成训练数据集和数值解决方案的准确性增强，达到测试分类错误低于$ 0.1 \％$ $ $ $ $ $ $ $ $ $ $ 7 \ times10^3 $和尺寸$ 3 \ times10^3 $的测试集。
We present a simulation-based classification approach for large deployed structures with localized operational excitations. The method extends the two-level Port-Reduced Reduced-Basis Component (PR-RBC) technique to provide faster solution estimation to the hyperbolic partial differential equation of time-domain elastodynamics with a moving load. Time-domain correlation function-based features are built in order to train classifiers such as artificial neural networks and perform damage detection. The method is tested on a bridge example with a moving vehicle (playing the role of a digital twin) in order to detect cracks' existence. Such problem has $45$ parameters and shows the merits of the two-level PR-RBC approach and of the correlation function-based features in the context of operational excitations, other nuisance parameters and added noise. The quality of the classification task is enhanced by the sufficiently large synthetic training dataset and the accuracy of the numerical solutions, reaching test classification errors below $0.1\%$ for disjoint training set of size $7\times10^3$ and test set of size $3\times10^3$.
