Fuzzy Clustering of Spatially Relevant Acoustic Data for Defect Detection

Fuzzy Clustering of Spatially Relevant Acoustic Data for Defect Detection
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用于缺陷检测的空间相关声学数据的模糊聚类

DOI:
10.1109/lra.2018.2820178
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发表时间:
2018
影响因子:
5.2
通讯作者:
Hajime Asama
Hajime Asama
中科院分区:
计算机科学2区
文献类型:
--
作者:
Jun Younes Louhi Kasahara;Hiromitsu Fujii;Atsushi Yamashita;Hajime Asama

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混凝土结构的有效诊断是一个日益增长的问题,在现代社会中,混凝土是一种无处不在的材料。锤击试验是一种传统的无损检测方法,在该领域中已经使用了很长时间,并且非常需要自动化。问题在于从锤子敲击结构表面后返回的声音中确定下面是否存在缺陷。在这封信中,我们提出了一种无监督学习方法的自动锤击测试。首先,均值漂移与梅尔频率倒谱系数在不同的参数值,以找到一个稳定的模式配置。然后,使用相应的峰值来获得空间模糊c均值的种子,以在组合音频和位置信息的同时对锤击样本进行聚类。在室内人工环境中,对含有人工缺陷的混凝土试块进行了试验。实验结果表明,该方法对不同类型和数量的缺陷检测具有较好的鲁棒性和有效性。我们的方法在室外环境中使用全自动锤击系统进行的测试中也显示出了良好的性能。
Efficient diagnosis of concrete structures is a growing issue in modern societies where concrete is an omnipresent material. The hammering test is a traditional nondestructive testing method that has been employed in the field for a long time and for which automation is highly desirable. The problem consists in determining from the sound returned after a hammer strike on a structure's surface if there is a defect beneath or not. In this letter, we present an unsupervised learning approach for the automation of hammering test. First, mean shift is used with Mel-frequency cepstrum coefficients at various parameter values in order to find a stable mode configuration. Then, the corresponding peaks are used to obtain seeds for spatial fuzzy c-means to cluster hammering samples while combining audio and position information. Experiments have been conducted in indoor artificial environment on concrete blocks containing man-made defects. Results showed the effectiveness and robustness of the proposed solution on detecting different types and number of defects. Our approach showed promising performance also in tests performed in outdoor environment using a fully automated hammering system.
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