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

机译:环境激励下结构损伤检测 - 使用特定间隔采样时域信号形成的符号三阶方阵

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摘要

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.
机译:在振动系统的结构健康监测中,不同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为损伤指标的符号三阶方阵损伤检测方法可以解决环境激励下损伤识别的难题。新的采样模式在自相关系数的帮助下,根据结构特性以特定间隔从时域中的信号中提取数据。然后,将提取的数据转换为符号并排列成三阶方阵,将矩阵的 Frobenius 范数作为可靠的损伤指标用于结构损伤识别;在这个过程中,采用传递率函数来消除可变激励的影响。首先,通过开裂的简支梁(模拟的 Abaqus 模型)验证了该方法。然后,实验室中的木桁架桥和环境激励下的实际工程场景共同证明了损伤识别方法的有效性和准确性,并证明了所提出的方法在环境激励下对不同类型的损伤具有鲁棒性。与其他相关方法相比,该方法更加直观和高效。

著录项

  • 期刊名称 Sensors (Basel Switzerland)
  • 作者

    Shuang Meng; Dongsheng Li;

  • 作者单位
  • 年(卷),期 2024(24),18
  • 年度 2024
  • 页码 5941
  • 总页数 19
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

    机译:结构损伤检测、符号三阶方阵、符号时间序列分析、环境激励;
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