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On time reversal-based signal enhancement for active lamb wave-based damage identification

机译:基于时间反转的信号增强,用于基于主动羔羊波的损伤识别

摘要

Lamb waves have been a promising candidate for quantitative damage identification for various engineering structures, taking advantage of their superb capabilities of traveling for long distances with fast propagation and low attenuation. However, the application of Lamb waves in damage identification so far has been hampered by the fact that the characteristic signals associated with defects are generally weaker compared with those arising from boundary reflections, mode conversions and environmental noises, making it a tough task to achieve satisfactory damage identification from the time series. With awareness of this challenge, this paper proposes a time reversal-based technique to enhance the strength of damage-scattered signals, which has been previously applied to bulk wave-based damage detection successfully. The investigation includes (i) an analysis of Lamb wave propagation in a plate, generated by PZT patches mounted on the structure; (ii) an introduction of the time reversal theory dedicated for waveform reconstruction with a narrow-band input; (iii) a process of enhancing damage-scattered signals based on time reversal focalization; and (iv) the experimental investigation of the proposed approach to enhance the damage identification on a composite plate. The results have demonstrated that signals scattered by delamination in the composite plate can be enhanced remarkably with the assistance of the proposed process, benefiting from which the damage in the plate is identified with ease and high precision.
机译:兰姆波凭借其出色的长距离传播,快速传播和低衰减的能力,已成为各种工程结构定量损伤识别的有前途的候选者。然而,到目前为止,由于与缺陷相关的特征信号通常比边界反射,模式转换和环境噪声所产生的特征信号要弱,因此妨碍了兰姆波在损伤识别中的应用。从时间序列中识别损坏。意识到这一挑战,本文提出了一种基于时间反转的技术来增强损伤分散信号的强度,该技术先前已成功地应用于基于体波的损伤检测。研究包括:(i)由安装在结构上的PZT贴片产生的Lamb波在平板中的传播分析; (ii)介绍了时间反转理论,专门用于窄带输入的波形重建; (iii)基于时间反转聚焦增强损害分散信号的过程; (iv)对建议的方法进行实验研究,以增强复合板上的损伤识别。结果表明,在所提出的方法的帮助下,复合板中通过分层而散射的信号可以得到显着增强,受益于此,可以容易且高精度地识别板中的损坏。

著录项

  • 作者

    Wang Q; Yuan S; Hong M; Su Z;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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