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Identification of nonlinear non-hysteretic and hysteretic structures using empirical mode decomposition.

机译:使用经验模态分解识别非线性非迟滞和迟滞结构。

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摘要

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of nonlinear intrinsic mode functions (IMFs). This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. However, the IMF components do not automatically guarantee a well-defined physical meaning hence, it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this study, EMD-based methods are developed to identify properties of nonlinear multi-degree-of-freedom (MDOF) structures. It is first shown that the EMD results of the displacement responses of a nonlinear non-hysteretic structure are numerical close to the nonlinear normal mode (NNM) responses while the EMD results of the displacement responses of a hysteretic structure are numerically close to nonlinear modal responses and offsets. Based on this agreement, two EMD-based identification techniques are developed to estimate the parameters of nonlinear non-hysteretic and hysteretic structures. The results of both numerical and experimental studies show that the two proposed EMD-based methods provide possible means for obtaining nonlinear properties in a structure.;It is known that the computational cost may be too expensive for the identification of full-scaled or detailed models of structures. Since the IMF components can be used as modal coordinates as well as provide estimates for responses at unmeasured locations if the mode shapes of the structure are known, the EMD-based technique is proposed for identifying and quantifying nonlinear behavior in damaged structures using incomplete measurement. Since the identification is performed in the modal domain, the proposed method is very efficient. Two procedures are developed for identifying nonlinear damages in the form of non-hysteresis and hysteresis in a structure. Both numerical and experimental studies show that the proposed method can be reasonably identified the type and physical location(s) of nonlinearity in a structure.
机译:经验模式分解(EMD)方法以将多分量信号分解为一组非线性固有模式函数(IMF)的能力而闻名。该方法为分解非线性和非平稳信号提供了一种有效且鲁棒的方法。但是,IMF组件并不能自动保证定义良好的物理含义,因此,在进行任何进一步的处理和解释之前,有必要仔细验证IMF组件。在这项研究中,开发了基于EMD的方法来识别非线性多自由度(MDOF)结构的属性。首先表明,非线性非滞后结构的位移响应的EMD结果在数值上接近于非线性正态响应(NNM),而滞后结构的位移响应的EMD结果在数值上接近于非线性模态响应。和偏移量。基于此协议,开发了两种基于EMD的识别技术来估计非线性非滞后和滞后结构的参数。数值和实验研究的结果表明,两种基于EMD的方法为获得结构的非线性特性提供了可能的方法。众所周知,计算成本对于识别完整或详细模型可能过于昂贵结构。如果已知结构的模态形状,则由于IMF组件可用作模态坐标并提供对未测量位置的响应的估计,因此提出了基于EMD的技术,用于使用不完整的测量来识别和量化受损结构中的非线性行为。由于识别是在模态域中进行的,因此该方法非常有效。开发了两种程序来识别结构中非磁滞和磁滞形式的非线性损伤。数值和实验研究均表明,所提出的方法可以合理地识别结构非线性的类型和物理位置。

著录项

  • 作者

    Poon, Chun Wing.;

  • 作者单位

    Hong Kong University of Science and Technology (Hong Kong).;

  • 授予单位 Hong Kong University of Science and Technology (Hong Kong).;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 197 p.
  • 总页数 197
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:40:30

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