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Acousto-ultrasonics-based fatigue damage characterization: Linear versus nonlinear signal features

机译：基于超声的疲劳损伤表征：线性与非线性信号特征

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摘要

Engineering structures are prone to fatigue damage over service lifespan, entailing early detection and continuous monitoring of the fatigue damage from its initiation through growth. A hybrid approach for characterizing fatigue damage was developed, using two genres of damage indices constructed based on the linear and the nonlinear features of acousto-ultrasonic waves. The feasibility, precision and practicability of using linear and nonlinear signal features, for quantitatively evaluating multiple barely visible fatigue cracks in a metallic structure, was compared. Miniaturized piezoelectric elements were networked to actively generate and acquire acousto-ultrasonic waves. The active sensing, in conjunction with a diagnostic imaging algorithm, enabled quantitative evaluation of fatigue damage and facilitated embeddable health monitoring. Results unveiled that the nonlinear features of acousto-ultrasonic waves outperform their linear counterparts in terms of the detectability. Despite the deficiency in perceiving small-scale damage and the possibility of conveying false alarms, linear features show advantages in noise tolerance and therefore superior practicability. The comparison has consequently motivated an amalgamation of linear and nonlinear features of acousto-ultrasonic waves, targeting the prediction of multi-scale damage ranging from microscopic fatigue cracks to macroscopic gross damage.
机译：工程结构在整个使用寿命中都容易出现疲劳损伤，这需要对疲劳损伤从产生到生长的整个过程进行早期检测和连续监控。开发了一种表征疲劳损伤的混合方法，该方法使用两种类型的损伤指数，这些损伤指数是基于声-超声波的线性和非线性特征构造的。比较了使用线性和非线性信号特征定量评估金属结构中多个几乎看不见的疲劳裂纹的可行性，精度和实用性。将小型化的压电元件联网以主动产生和获取声-超声波。主动感应与诊断成像算法相结合，可以对疲劳损伤进行定量评估，并促进可嵌入的健康监测。结果表明，在可检测性方面，声-超声波的非线性特征优于线性特征。尽管缺乏感知小规模损坏的可能性，并且可能会传送错误警报，但线性特征在噪声容忍度方面显示出优势，因此具有较高的实用性。因此，该比较促使声-超声波的线性和非线性特征融合在一起，目标是预测从微观疲劳裂纹到宏观总损伤的多尺度损伤。

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  • 来源
    《Mechanical systems and signal processing》 |2014年第1期|225-239|共15页
  • 作者

    Zhongqing Su; Chao Zhou; Ming Hong; Li Cheng; Qiang Wang; Xinlin Qing;

  • 作者单位

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

    School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, Guangdong Province 510006, PR China;

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

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

    College of Automation, Nanjing University of Posts and Telecommunications, Nanjing PR China;

    Division of Aviation Health and Safety Management, Beijing Aeronautical Science and Technology Research Institute of COMAC, Beijing 100083, PR China;

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

    Fatigue damage characterization; Acousto-ultrasonics; Nonlinear signal features; Linear signal features; Piezoelectric sensor network; Structural health monitoring;

    机译：疲劳损伤表征;声-超声;非线性信号特征;线性信号功能;压电传感器网络;结构健康监测;

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