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基于特定间隔采样时域信号构成的符号三阶方阵的环境激励下结构损伤检测

Structural Damage Detection under Ambient Excitation Using Symbolic Three-Order Square Matrix Formed by Specific-Interval-Sampled Time-Domain Signals.

作者信息

Meng Shuang, Li Dongsheng

机构信息

Department of Civil Engineering, Dalian University of Technology, Dalian 116024, China.

MOE Key Laboratory of Intelligent Manufacturing Technology, Shantou University, Shantou 515063, China.

出版信息

Sensors (Basel). 2024 Sep 13;24(18):5941. doi: 10.3390/s24185941.

Abstract

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam-a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.

摘要

在振动系统的结构健康监测中,变化的激励总是会影响损伤识别的准确性。所提出的以矩阵范数作为损伤指标的符号三阶方阵损伤检测方法,能够解决环境激励下损伤识别的难题。新的采样模式借助自相关系数,基于结构特性在特定时间间隔从时域信号中提取数据。然后,将提取的数据转换为符号并排列成三阶方阵,矩阵的弗罗贝尼乌斯范数用作结构损伤识别的可靠损伤指标。在此过程中,采用传递函数来消除变化激励的影响。首先,通过一个开裂简支梁——Abaqus模拟模型对该方法进行了验证。然后,实验室中的一座木桁架桥以及环境激励下的实际工程场景共同证明了该损伤识别方法的有效性和准确性,并证明了所提方法在环境激励下对不同类型损伤具有鲁棒性。与其他相关方法相比,该方法更加直观高效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c94f/11436037/c53873bfac02/sensors-24-05941-g001.jpg

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