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GUW-based structural damage detection using WPT statistical features and multiclass SVM

机译:使用WPT统计特征和多类SVM的基于GUW的结构损伤检测

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摘要

Recently, guided ultrasonic waves (GUW) are widely used for damage detection in structural health monitoring (SHM) of different engineering structures. In this study, an intelligent damage detection method is proposed to be used in SHM applications. At first, GUW signal is de-noised by discrete wavelet transform (DWT). After that, wavelet packet transform (WPT) is employed to decompose the de-noised signal and the statistical features of decomposed packets are extracted as damage-sensitive features. Finally, a multiclass support vector machine (SVM) classifier is used to detect the damage and estimate its severity. The proposed method is employed for GUW-based structural damage detection of a thick steel beam. The effects of different parameters on the sensitivity of the method are surveyed. Furthermore, by comparing with some other similar algorithms, the performance of the proposed method is verified. The experimental results demonstrate that the proposed method can appropriately detect a structural damage and estimate its severity.
机译:近来,在不同工程结构的结构健康监测(SHM)中,导波(GUW)被广泛用于损伤检测。在这项研究中,提出了一种在SHM应用中使用的智能损伤检测方法。首先,通过离散小波变换(DWT)对GUW信号进行消噪。之后,采用小波包变换(WPT)对降噪后的信号进行分解,提取出分解后的包的统计特征作为损伤敏感特征。最后,使用多类支持向量机(SVM)分类器来检测损害并估计其严重性。该方法用于基于GUW的厚钢梁结构损伤检测。考察了不同参数对方法灵敏度的影响。此外,通过与其他一些类似的算法进行比较,验证了该方法的性能。实验结果表明,该方法可以适当地检测出结构损伤并评估其严重程度。

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