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首页> 外文期刊>Journal of Nondestructive Evaluation >Wavelet Based Clustering of Acoustic Emission Hits to Characterize Damage Mechanisms in Composites
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Wavelet Based Clustering of Acoustic Emission Hits to Characterize Damage Mechanisms in Composites

机译：基于小波的声发射击中的聚类，以表征复合材料中的损坏机制

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Clustering data is an important topic in acoustic emission (AE) health monitoring. Identification of damage mechanisms in composites via AE technique requires an automatic process of classification. In this work, we propose a novel and simple supervised method to discriminate AE signals produced by fracture mechanisms in polymer composites. The novelty of this work is to propose new pertinent descriptors offered by using the continuous wavelet transform, where signals of learning are decomposed and the corresponding wavelet coefficients are calculated. In addition, the entropy criterion is applied to select the most correlated wavelets associated to each failure mechanism. This process allows to establish a filter in the form of vectors for each class of signals and descriptors denote the reconstruction errors calculated by involving the filter associated to each damage mechanism. The k-means algorithm is executed to calculate the center of each class. The technique is applied to AE signals recorded from specific mechanical tests to demonstrate the performance of the proposed descriptors.
机译：聚类数据是声学发射（AE）健康监测中的一个重要主题。通过AE技术识别复合材料中的损伤机制需要自动分类过程。在这项工作中，我们提出了一种新颖且简单的监督方法来区分由聚合物复合材料中的断裂机制产生的AE信号。这项工作的新颖性是提出通过使用连续小波变换提供的新相关描述符，其中计算学习信号并计算相应的小波系数。另外，熵标准应用于选择与每个故障机制相关联的最相关的小波。该过程允许以每个类信号的矢量形式建立滤波器，并且描述符表示通过涉及与每个损坏机制相关联的滤波器计算的重建误差。执行K-means算法以计算每个类的中心。该技术应用于从特定机械测试记录的AE信号，以演示所提出的描述符的性能。

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