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引用次数: 0

摘要

超声检测技术在金属结构健康监测中得到了广泛的应用。将微小裂纹信息隐藏在超声信号中,有利于损伤状态的检测和获取。虽然已经有大量的研究,但鲁棒性小裂纹特征的提取仍然是一个基本问题。本文提出了一种基于接收信号小波包变换的裂纹识别算法。通过计算子带信号能量,确定最合适的分解电平。然后,利用受损信号与未受损信号的相关系数来定义特征。主成分分析(PCA)通过减少重叠和冗余的特征来实现特征提取。最后,将提取的特征输入支持向量机(SVM)分类器,并利用其输出对损伤类型进行分类。通过实际实验验证了该方法的有效性。结果表明,与其他方法相比,该算法具有更高的识别精度和更强的鲁棒性特征。
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A novel method of feature extraction for minor crack identification
Ultrasonic testing technique has been widely applied for monitoring the metal structure health. It is useful to detect and access the damage condition with the minor crack information being concealed in the ultrasonic signal. Although there has been a large amount of studies, the extraction of robust minor crack features is still a fundamental problem. In this paper, a novel crack identification algorithm is proposed by the wavelet packet transform (WPT) of received signal. With the calculation of sub-band signal energy, the most suitable decomposition level is decided. Then, the features are defined by the correlation coefficient between the damaged signal and undamaged signal. With principal component analysis (PCA), the feature extraction is achieved by reducing the overlapped and redundant ones. Finally, the extracted features are fed into support vector machines (SVM) classier and their outputs are employed to classify the damage type. The performance of the proposed method is confirmed with practical experiment. It indicated that compared with other methods, the proposed algorithm has a higher identification accuracy with more robust features.
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