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Damage Detection in CFRP Using Wavelet Scale Correlation

机译:小波尺度相关的CFRP损伤检测

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摘要

Degradation of a material affects modal properties such as natural frequencies and mode shapes. These parameters show potential for global damage detection. A recent study of the propagation of flexural waves through an elastic solid has revealed the potential to determine localised cracks and in carbon fibre reinforced plastics (CFRP) delaminations. The continuous wavelet transform has provided useful insight into the damage mechanism allowing the signal to be decomposed into a series of time-frequency bands. Estimation of the group velocity of flexural waves for specific frequencies allows crack position to be identified through the analysis of the first few wave peaks. In this region waves reflected from the crack surface are clearly visible. Crack detection ability is a function of the propagating wave frequency, higher frequencies being more sensitive to small cracks. For application purposes this requires high frequency interrogation to provide sufficient resolution; in this study the acoustic range is selected (20 kHz to 1 GHz). This paper studies the identification of cracks in CFRP beams. The study seeks to identify the location of key data in the signal to provide rapid signal processing suitable for real-time application. Owing to the sensitivity of frequency data to structural changes, wavelet scales of damaged structures are correlated with those of the undamaged structure providing a measure of the changes incurred by the sample. Appropriate data reduction is undertaken for application to a neural network. Results demonstrate a high degree of accuracy in the determination of crack magnitude.
机译:材料的降解会影响模态特性,例如固有频率和模态形状。这些参数显示了潜在的整体损坏检测能力。弯曲波在弹性固体中传播的最新研究表明,确定局部裂纹和碳纤维增强塑料(CFRP)分层的潜力。连续小波变换对破坏机制提供了有用的见解,从而使信号可以分解为一系列时间频带。对于特定频率的弯曲波群速度的估计,可以通过分析前几个波峰来确定裂纹位置。在该区域中,从裂纹表面反射的波清晰可见。裂纹检测能力是传播波频率的函数,较高的频率对小裂纹更敏感。出于应用目的,这需要高频询问以提供足够的分辨率。在本研究中,选择了声学范围(20 kHz至1 GHz)。本文研究了CFRP梁裂缝的识别。该研究旨在确定关键数据在信号中的位置,以提供适用于实时应用的快速信号处理。由于频率数据对结构变化的敏感性,将受损结构的小波尺度与未损坏结构的小尺度相关联,从而提供了样本发生变化的度量。进行了适当的数据缩减以应用于神经网络。结果表明,确定裂纹大小的准确性很高。

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