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Locating fatigue damage using temporal signal features of nonlinear Lamb waves

机译:利用非线性兰姆波的时间信号特征定位疲劳损伤

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摘要

The temporal signal features of linear guided waves, as typified by the time-of-flight (ToF), have been exploited intensively for identifying damage, with proven effectiveness in locating gross damage in particular. Upon re-visiting the conventional, ToF-based detection philosophy, the present study extends the use of temporal signal processing to the realm of nonlinear Lamb waves, so as to reap the high sensitivity of nonlinear Lamb waves to small-scale damage [e.g., fatigue cracks), and the efficacy of temporal signal processing in locating damage. Nonlinear wave features (i.e., higher-order harmonics) are extracted using networked, miniaturized piezoelectric wafers, and reverted to the time domain for damage localization. The proposed approach circumvents the deficiencies of using Lamb wave features for evaluating undersized damage, which are either undiscernible in time-series analysis or lacking in temporal information in spectral analysis. A probabilistic imaging algorithm is introduced to supplement the approach, facilitating the presentation of identification results in an intuitive manner. Through numerical simulation and then experimental validation, two damage indices (DIs) are comparatively constructed, based, respectively, on linear and nonlinear temporal features of Lamb waves, and used to locate fatigue damage near a rivet hole of an aluminum plate. Results corroborate the feasibility and effectiveness of using temporal signal features of nonlinear Lamb waves to locate small-scale fatigue damage, with enhanced accuracy compared with linear ToF-based detection. Taking a step further, a synthesized detection strategy is formulated by amalgamating the two DIs, targeting continuous and adaptive monitoring of damage from its onset to macroscopic formation.
机译:以飞行时间(ToF)为代表的线性导波的时间信号特征已被广泛地用于识别损伤,特别是在确定严重损伤方面已被证明是有效的。在重新研究基于ToF的常规检测原理后,本研究将时间信号处理的使用扩展到非线性Lamb波领域,从而获得了非线性Lamb波对小规模损伤的高灵敏度[例如,疲劳裂纹),以及时间信号处理在定位损坏中的功效。使用联网的小型化压电晶片提取非线性波特征(即高次谐波),并将其恢复到时域以进行损伤定位。所提出的方法规避了使用兰姆波特征来评估尺寸过小的损伤的缺陷,这些缺陷在时间序列分析中无法区分,或者在频谱分析中缺乏时间信息。引入概率成像算法来补充该方法,以直观的方式促进识别结果的呈现。通过数值模拟然后进行实验验证,分别基于兰姆波的线性和非线性时间特征,比较地构造了两个损伤指数(DI),并将其用于定位铝板铆钉孔附近的疲劳损伤。结果证实了使用非线性兰姆波的时间信号特征来定位小规模疲劳损伤的可行性和有效性,与基于线性ToF的检测相比,其准确性更高。更进一步,通过将两个DI融合在一起,制定了一种合成的检测策略,目标是对从发病到宏观形成的损害进行连续和自适应的监测。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2015年第8期|182-197|共16页
  • 作者单位

    Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong ,Department of Civil Engineering, Monash University, Clayton 3800, VIC, Australia;

    The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, PR China ,Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;

    Department of Civil Engineering, Monash University, Clayton 3800, VIC, Australia;

    Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, South Korea;

    Department of Aviation Health and Safety Management, Beijing Aeronautical Science and Technology Research Institute of COMAC, Beijing 102211, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Temporal signal features; Nonlinear Lamb waves; Signal processing; Fatigue damage; Sparse sensor network; Structural health monitoring;

    机译:时间信号特征;非线性兰姆波;信号处理;疲劳损伤;稀疏的传感器网络;结构健康监测;

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