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首页> 外文期刊>Applied Acoustics >Unsupervised clustering for building a learning database of acoustic emission signals to identify damage mechanisms in unidirectional laminates
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Unsupervised clustering for building a learning database of acoustic emission signals to identify damage mechanisms in unidirectional laminates

机译：无监督聚类，用于建立声发射信号的学习数据库，以识别单向层压板中的损伤机理

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摘要

This work aims at developing a new clustering method of acoustic emission (AE) signals, called Incremental Clustering (IC), induced by damage mechanisms of glass fibre reinforced composite materials. The advantage of this method over other methods from literature is the capability to identify the signals carrying information and to provide the types of damage mechanisms without using additional expertise. To apply the method based on actual data, several specimens of glass fibre reinforced epoxy composites have been manufactured and subjected to specific mechanical tests. The developed method was compared to the k-means method that is extensively used to classify the AE signals. The reliability of the learning database was checked by the performance evaluation of the k Nearest Neighbours' (kNN) classifiers. The kNN classifiers were trained by the training dataset and evaluated by the test dataset. The area under the receiver operating characteristic curves (AUC) was used as a criterion for evaluating the performance of classifiers. From specific mechanical tests, the IC method presented more advantages to successfully classify the AE signals and build a labelled learning database than the k-means method. The chronology of appearance of different damage mechanisms demonstrated the effectiveness of the incremental method. The powerful performance of the supervised method, characterized by values of AUC greater than 0.9 for each damage type, confirmed the reliability of the obtained learning database. (C) 2017 Elsevier Ltd. All rights reserved.
机译：这项工作旨在开发一种新的声发射（AE）信号聚类方法，称为增量聚类（IC），它是由玻璃纤维增​​强复合材料的损伤机理引起的。与文献中的其他方法相比，此方法的优势在于能够识别携带信息的信号并提供损坏机制的类型，而无需使用其他专业知识。为了基于实际数据应用该方法，已经制造了一些玻璃纤维增​​强的环氧复合材料样品，并进行了特定的机械测试。将开发的方法与广泛用于对AE信号进行分类的k均值方法进行了比较。通过对k个最近邻（kNN）分类器的性能评估来检查学习数据库的可靠性。 kNN分类器由训练数据集进行训练，并由测试数据集进行评估。接收器工作特性曲线（AUC）下的面积用作评估分类器性能的标准。通过特定的机械测试，IC方法比k均值方法具有更多的优点，可以成功地对AE信号进行分类并建立标记的学习数据库。不同损伤机制出现的时间顺序证明了增量方法的有效性。有监督方法的强大性能，其特征在于每种损坏类型的AUC值均大于0.9，从而证实了所获得学习数据库的可靠性。 （C）2017 Elsevier Ltd.保留所有权利。

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