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Structural Damage Detection Using Empirical Mode Decomposition and HHT

机译:基于经验模态分解和HHT的结构损伤检测

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摘要

The importance of developing robust systems that can detect and locate progressive deterioration in structures or abrupt damage induced by extreme loading events is well recognized in the field of structural health monitoring. A reliable damage detection algorithm is the critical element of this development. This paper discusses a new time-frequency data analysis method, Empirical Mode Decomposition (EMD) and Hilbert-Huang Transformation (HHT), and its application to damage detection. The time series data from numerical simulation and laboratory experiments of a simple structure with and without damage are processed to determine the presence and location of structural damage. This is done by extracting a set of basis components directly from the measured response of a system and tracking phase properties between successive degrees of freedom of the structure. Our results illustrate that this new approach, along with simple physics-based models, permits the development of a reliable damage detection methodology.
机译:在结构健康监测领域中,开发健壮的系统的重要性非常重要,该系统可以检测并定位结构的逐步恶化或极端载荷事件引起的突然破坏。可靠的损坏检测算法是此开发的关键要素。本文讨论了一种新的时频数据分析方法,即经验模态分解(EMD)和希尔伯特-黄变换(HHT),并将其应用于损伤检测。处理来自具有和不具有损坏的简单结构的数值模拟和实验室实验的时间序列数据,以确定结构损坏的存在和位置。这是通过直接从系统的测量响应中提取一组基础分量并跟踪结构的连续自由度之间的相位属性来完成的。我们的结果表明,这种新方法与简单的基于物理的模型一起,允许开发可靠的损坏检测方法。

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