Damage Identification for The Tree-like Network through Frequency-domain Modeling

Damage Modeling for the Tree-Like Network with Fractional-Order Calculus

In this paper, we propose that a tree-like network with damage can be modeled as the product of a fractional-order nominal plant and a fractional-order multiplicative disturbance, which is well structured and completely characterized by the damage amount at each damaged component. Such way of modeling brings us insights about that damaged network's behavior, helps us design robust controllers under uncertain damages and identify the damage. Although the main result in this paper is specialized to one model, we believe that this way of constructing a well-structured disturbance model can be app…

考虑最优传感器布置的数字孪生框架下海上导管架平台损伤识别

Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

本研究提出融合最优传感器布置的数字孪生框架，采用MOLA优化传感器配置、MCMC-贝叶斯方法量化参数不确定性并识别损伤，经海上导管架平台验证可有效定位损伤并评估严重程度。

基于图的视觉衍生动态模态表征用于结构损伤识别

Graph-Based Characterization of Vision-Derived Dynamic Modes for Structural Damage Identification

本文提出图论框架，利用延迟嵌入动态模式分解从视频测量构建振动梁的图，以图拓扑特征（如连通性）作为损伤敏感度量，实验与数值研究验证了随裂纹深度增加连通性单调下降，为视觉结构损伤识别提供新基础。

多输入切向插值驱动的喷气教练机损伤检测

Multiple input tangential interpolation-driven damage detection of a jet trainer aircraft

本文提出将改进Loewner框架（iLF）用于结构健康监测的模态识别，并结合修正总模态保证准则（MTMAC）与坐标模态保证准则（COMAC），在BAE Systems Hawk T1A喷气教练机地面振动试验数据上实现了有效的损伤检测、定位与评估。

集成两阶段深度学习工具用于飓风后快速损伤识别与修复调度

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

提出两阶段深度学习工具，第一阶段识别受损线路，第二阶段计算修复调度，在IEEE 9500节点测试馈线上实现高精度快速决策支持。

Sparse representation for damage identification of structural systems

Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and modeling error. Sparse representation can be used to perform inverse analysis for the case of sparse damage. In this paper, we propose a novel two-stage sensitivity analysis-based framework for both model updating and sparse damage identification. Specifically, an $\ell_2$ Bayesian learning method is firstly developed for updating the intact model and uncertainty quantification so as to set forward …