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Numerical and experimental study on improving diagnosis in structural health monitoring.

机译：改善结构健康监测诊断的数值和实验研究。

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摘要

Structural Health Monitoring (SHM) is a procedure of assessing structural integrity during its service period to improve maintenance in terms of cost and reliability. In SHM for aircraft applications, crack identification using reflected ultrasonic Lamb waves from a crack is one of the most active research areas. In addition to detecting a crack, estimating its size is important for judging the severity of the damage, and this can be done by various techniques. Specifically, we focus on the relationship between the sensor signal amplitude and crack size through experiments and simulation for help in size estimation. The maximum received signal amplitude is found to vary linearly from simulation and this agrees with measurements with crack size up to 30 mm.;However, SHM using embedded sensors have limitation in terms of accuracy of detection. Our approach to overcome this is as follows. If measurements are frequently performed using the above mentioned techniques while the crack grows, then a better estimation of crack size may be possible by analyzing sensor signals for the same crack location at different sizes.;The main objective of this research is to improve the accuracy of current diagnosis by using the prediction from previous inspection results. Unlike manual inspection, SHM can take frequent measurements and trace crack growth. By taking advantage of this aspect, higher accuracy about current crack size can be achieved. First, using the previous SHM measurements and the crack propagation model, we predict the statistical distribution of crack sizes at the next SHM inspection cycle. Then, this predicted distribution is combined with the SHM measurement at the next cycle by using the Bayesian approach for more precise estimate. The propagated distribution from the previous inspection is used as a prior and the variability at the current inspection is used to build the likelihood function. The uncertainty in measurements is modeled by a lognormal distribution. Results show substantial improvements in accuracy.
机译：结构健康监测（SHM）是评估其服务期内的结构完整性以提高维护成本和可靠性的一种程序。在用于飞机的SHM中，利用来自裂缝的反射超声波兰姆波来识别裂缝是最活跃的研究领域之一。除了检测裂纹外，估计裂纹的大小对于判断损坏的严重程度也很重要，这可以通过各种技术来完成。具体来说，我们通过实验和仿真关注传感器信号幅度与裂缝尺寸之间的关系，以帮助估算尺寸。发现最大接收信号幅度与模拟值呈线性变化，这与裂纹尺寸最大为30 mm的测量结果是一致的。但是，使用嵌入式传感器的SHM在检测精度方面存在局限性。我们克服这一问题的方法如下。如果在裂纹扩展时经常使用上述技术进行测量，则可以通过分析不同尺寸下相同裂纹位置的传感器信号来更好地估计裂纹尺寸。;本研究的主要目的是提高准确性通过使用先前检查结果的预测来确定当前诊断。与手动检查不同，SHM可以进行频繁的测量并追踪裂纹的扩展。通过利用这个方面，可以实现关于当前裂纹尺寸的更高的精度。首先，使用先前的SHM测量值和裂纹扩展模型，我们预测在下一个SHM检查周期中裂纹尺寸的统计分布。然后，通过使用贝叶斯方法对下一周期的预测分布与SHM测量值进行组合，以进行更精确的估计。来自先前检查的传播分布用作先前检查，并且使用当前检查的变异性来建立似然函数。测量的不确定性通过对数正态分布建模。结果表明准确性有了实质性的提高。

著录项

  • 作者

    An, Jungeun.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Engineering Aerospace.;Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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