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首页> 外文期刊>Arabian Journal for Science and Engineering >Identification of Failure Modes in Composites from Clustered Acoustic Emission Data Using Pattern Recognition and Wavelet Transformation
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Identification of Failure Modes in Composites from Clustered Acoustic Emission Data Using Pattern Recognition and Wavelet Transformation

机译：使用模式识别和小波变换从聚集的声发射数据中识别复合材料的失效模式

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摘要

Acoustic emission (AE) is widely used to characterize damage occurring in composite materials: however, the discrimination between AE signatures due to different damage mechanisms is still an open issue. In this study, the various failure mechanisms in bidirectional glass/epoxy laminates subjected to uni-axial tension are identified using AE monitoring. AE data recorded during the tensile testing of a single-layer specimen are used to identify matrix cracking and fiber failure. In contrast, delamination signals are characterized using a two-layer specimen with a pre-induced defect, produced by artificially inserting a 10 mm wide Teflon tape in the middle portion of the two layers. Twelve-layer Glass fiber reinforced plastics laminates were also tested as a reference for the comparison of results. The procedure leading to signal discrimination involves a number of steps. First, Fuzzy C-means clustering associated with principal component analysis are used to discriminate between failure mechanisms, while parametric studies using AE count rate and cumulative counts allowed damage discrimination at various stages of loading. The two above methods led to AE waveform selection: on the selected waveforms, fast Fourier transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform (WT) allowed identifying frequency range and time history for failure modes in each signal, while noise content associated with the different failure modes is calculated and removed by dis-crete WT. Short time FFT (STFFT) finally highlighted the possible failure mechanism associated with each signal.
机译：声发射（AE）被广泛用于表征复合材料中发生的损坏：但是，由于损坏机制不同而导致AE签名之间的区别仍然是一个未解决的问题。在这项研究中，使用AE监测可以确定双向玻璃/环氧树脂层压板承受单轴张力的各种破坏机理。在单层样品的拉伸测试过程中记录的AE数据用于识别基体开裂和纤维破坏。相比之下，分层信号的特征是使用两层带有预诱发缺陷的样本，该样本是通过在两层的中间部分人为插入10毫米宽的特氟隆胶带而产生的。还对十二层玻璃纤维增​​强塑料层压板进行了测试，以作为结果比较的参考。导致信号区分的过程涉及许多步骤。首先，与主成分分析相关的模糊C均值聚类用于区分故障机制，而使用AE计数率和累积计数的参数研究可以在载荷的各个阶段进行损伤识别。以上两种方法导致了AE波形的选择：在所选的波形上，启用了快速傅立叶变换（FFT）来计算每种损坏机制的频率含量。连续小波变换（WT）可以识别每个信号中故障模式的频率范围和时间历史，而与不同故障模式相关的噪声含量则由离散WT计算并消除。短时FFT（STFFT）最终强调了与每个信号相关的可能的故障机制。

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