Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers
Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers
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DOI：
10.1016/j.ndteint.2003.09.010
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发表时间：
2004-06-01
影响因子：
4.2
通讯作者：
Salmon, L
中科院分区：
文献类型：
--
作者：
Godin, N;Huguet, S;Salmon, L
声发射（AE）可以用来区分不同类型的损伤发生在约束复合材料。然而，与数据分析相关的主要问题是不同声发射源之间的区分。聚类分析的目的是将一组数据分成反映数据内部结构的几个类。事实上，聚类分析是调查和解释数据的重要工具。在本文中，我们使用两种分类器：监督分类器和无监督分类器（Kohonen映射）。我们结合联合收割机两种技术：k-means算法和k近邻。玻璃/聚酯模型标本用于验证所提出的方法。我们工作的聚酯树脂和玻璃/聚酯单向试样，在不同的配置进行拉伸载荷，等待优先损伤模式的材料。此外，单纤维复合材料已被测试，以产生纤维断裂声发射事件的条件下，非常接近那些遇到的真实的复合材料。(C)2003 Elsevier Ltd.保留所有权利。
Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in a constrained composite. However, the main problem associated with data analysis is the discrimination between the different acoustic emission sources. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of the data. Indeed, cluster analysis is an important tool for investigating and interpreting data. In this paper we use two kinds of classifiers: a supervised classifier and also an unsupervised one (Kohonen's map). We combine two techniques: the k-means algorithm and the k nearest neighbours. Glass/polyester model specimens were used for the validation of the proposed methodology. We worked on polyester resin and glass/polyester unidirectional specimens, subjected to tensile loading within different configurations, awaiting preferential damage modes in the material. Moreover, single fibre composites have been tested to produce fibre breakage acoustic emission events under conditions closely approximating those encountered in a real composite. (C) 2003 Elsevier Ltd. All rights reserved.