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Sparse and Dispersion-Based Matching Pursuit for Minimizing the Dispersion Effect Occurring When Using Guided Wave for Pipe Inspection

机译:基于稀疏和色散的匹配追踪用于最小化使用导波进行管道检查时发生的色散效应

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摘要

Ultrasonic guided wave is an effective tool for structural health monitoring of structures for detecting defects. In practice, guided wave signals are dispersive and contain multiple modes and noise. In the presence of overlapped wave-packets/modes and noise together with dispersion, extracting meaningful information from these signals is a challenging task. Handling such challenge requires an advanced signal processing tool. The aim of this study is to develop an effective and robust signal processing tool to deal with the complexity of guided wave signals for non-destructive testing (NDT) purpose. To achieve this goal, Sparse Representation with Dispersion Based Matching Pursuit (SDMP) is proposed. Addressing the three abovementioned facts that complicate signal interpretation, SDMP separates overlapped modes and demonstrates good performance against noise with maximum sparsity. With the dispersion taken into account, an overc-omplete and redundant dictionary of basic atoms based on a narrowband excitation signal is designed. As Finite Element Method (FEM) was used to predict the form of wave packets propagating along structures, these atoms have the maximum resemblance with real guided wave signals. SDMP operates in two stages. In the first stage, similar to Matching Pursuit (MP), the approximation improves by adding, a single atom to the solution set at each iteration. However, atom selection criterion of SDMP utilizes the time localization of guided wave reflections that makes a portion of overlapped wave-packets to be composed mainly of a single echo. In the second stage of the algorithm, the selected atoms that have frequency inconsistency with the excitation signal are discarded. This increases the sparsity of the final representation. Meanwhile, leading to accurate approximation, as discarded atoms are not representing guided wave reflections, it simplifies extracting physical meanings for defect detection purpose. To verify the effectiveness of SDMP for damage detection results from numerical simulations and experiments on steel pipes are presented.
机译:超声波导波是一种有效的工具,用于监视结构的结构健康状况以检测缺陷。实际上,导波信号是分散的,并且包含多种模式和噪声。在存在重叠的波包/模式和噪声以及色散的情况下,从这些信号中提取有意义的信息是一项艰巨的任务。处理此类挑战需要先进的信号处理工具。这项研究的目的是开发一种有效而强大的信号处理工具,以处理用于无损检测(NDT)的导波信号的复杂性。为了实现这一目标,提出了基于色散的基于匹配追踪的稀疏表示(SDMP)。针对上述使信号解释复杂化的三个事实,SDMP分离了重叠的模式,并以最大的稀疏性表现出良好的抗噪声性能。考虑到色散,设计了基于窄带激发信号的基本原子的过度冗余的字典。由于使用有限元方法(FEM)来预测沿结构传播的波包的形式,因此这些原子与实际的导波信号具有最大的相似性。 SDMP分两个阶段运行。在第一阶段,类似于匹配追踪(MP),通过在每次迭代中向解决方案集中添加单个原子来提高近似值。但是,SDMP的原子选择标准利用了导波反射的时间局部化,这使得一部分重叠波包主要由单个回波组成。在算法的第二阶段,将舍弃与激发信号频率不一致的所选原子。这增加了最终表示的稀疏性。同时,由于被丢弃的原子不代表导波反射,因此导致了精确的近似,从而简化了出于缺陷检测目的而提取物理含义的过程。为了验证SDMP对钢管损伤检测结果的有效性,提出了数值模拟和钢管实验。

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