arXiv:2509.16216cs.CEcs.LG2025-09

用少量非破坏性测量,精准定位金属棒内部缺陷位置与强度。

On the Detection of Internal Defects in Structured Media

  • 基于波动方程与拉普拉斯变换,建立缺陷特征与测量数据的数学映射。
  • 通过优化残差函数,准确反演出缺陷位置和刚度变化量。
  • 适合结构健康监测、无损检测领域研究人员参考。

评估金属棒或声学管道等结构的完整性时,检测内部裂纹(缺陷)是一项关键挑战。传统方法依赖听觉与视觉判断,不进行物理拆解。本研究提出一种基于少量非侵入式测量的缺陷成像策略。假设一维模型(如长细杆中的纵波或弦的横向振动),采用连续一维波动方程建模物理现象,并结合拉普拉斯变换与优化技术实现缺陷识别。重点研究均匀棒体中局部存在杨氏模量异常的情况,先将问题等价为弹簧-质量振动系统,再利用拉普拉斯域中缺陷特性与测量数据间的解析映射关系。使用MATLAB生成多种场景下的合成数据(含任意位置与刚度的单个缺陷),结合解析映射构建残差函数,经优化后可精确反演缺陷位置与强度。

原文摘要 · Abstract (English)

A critical issue that affects engineers trying to assess the structural integrity of various infrastructures, such as metal rods or acoustic ducts, is the challenge of detecting internal fractures (defects). Traditionally, engineers depend on audible and visual aids to identify these fractures, as they do not physically dissect the object in question into multiple pieces to check for inconsistencies. This research introduces ideas towards the development of a robust strategy to image such defects using only a small set of minimal, non-invasive measurements. Assuming a one dimensional model (e.g. longitudinal waves in long and thin rods/acoustic ducts or transverse vibrations of strings), we make use of the continuous one-dimensional wave equation to model these physical phenomena and then employ specialized mathematical analysis tools (the Laplace transform and optimization) to introduce our defect imaging ideas. In particular, we will focus on the case of a long bar which is homogeneous throughout except in a small area where a defect in its Young's modulus is present. We will first demonstrate how the problem is equivalent to a spring-mass vibrational system, and then show how our imaging strategy makes use of the Laplace domain analytic map between the characteristics of the respective defect and the measurement data. More explicitly, we will utilize MATLAB (a platform for numerical computations) to collect synthetic data (computational alternative to real world measurements) for several scenarios with one defect of arbitrary location and stiffness. Subsequently, we will use this data along with our analytically developed map (between defect characteristics and measurements) to construct a residual function which, once optimized, will reveal the location and magnitude of the stiffness defect.

无损检测缺陷成像波动方程优化

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