局部激励下大型展开结构健康监测的模型降阶方法
Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations
摘要：
我们提出了一个基于仿真的分类方法，大型部署结构与本地化的操作激励。该方法扩展了两层端口缩减缩减基分量（PR-RBC）技术，为移动荷载作用下的时域弹性动力学双曲型偏微分方程提供了更快的解估计。建立基于时域相关函数的特征，以便训练分类器，如人工神经网络和执行损伤检测。该方法进行了测试，在一个桥梁的例子与移动的车辆（扮演的角色的数字孪生），以检测裂缝的存在。这样的问题具有$45$参数，并且在操作激励、其他冗余参数和添加的噪声的背景下示出了两级PR-RBC方法和基于相关函数的特征的优点。分类任务的质量通过足够大的合成训练数据集和数值解的准确性来增强，对于大小为$7\times10^3$的不相交训练集和大小为$3\times10^3$的测试集，达到低于$0.1\%$的测试分类误差。
英文摘要：
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 $3\times10^3$.
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