Rostami Javad, Tse Peter W T, Fang Zhou
Smart Engineering Asset Management Laboratory Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
Materials (Basel). 2017 Jun 6;10(6):622. doi: 10.3390/ma10060622.
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.
超声导波是用于结构健康监测以检测结构缺陷的有效工具。在实际应用中，导波信号具有色散特性，包含多种模式和噪声。在存在重叠波包/模式以及噪声和色散的情况下，从这些信号中提取有意义的信息是一项具有挑战性的任务。应对这一挑战需要先进的信号处理工具。本研究的目的是开发一种有效且强大的信号处理工具，以处理导波信号的复杂性，用于无损检测（NDT）目的。为实现这一目标，提出了基于色散匹配追踪的稀疏表示（SDMP）方法。针对上述使信号解释复杂化的三个事实，SDMP分离重叠模式，并在最大稀疏性下对噪声表现出良好性能。考虑到色散，基于窄带激励信号设计了一个超完备且冗余的基本原子字典。由于使用有限元方法（FEM）来预测沿结构传播的波包形式，这些原子与实际导波信号具有最大相似性。SDMP分两个阶段运行。在第一阶段，类似于匹配追踪（MP），通过在每次迭代时向解集添加单个原子来改进近似。然而，SDMP的原子选择标准利用了导波反射的时间定位，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，丢弃与激励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，由于丢弃的原子不代表导波反射，从而导致精确近似，简化了为缺陷检测目的提取物理意义的过程。为验证SDMP用于损伤检测的有效性，给出了钢管数值模拟和实验的结果。
Zotero 插件
