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Acoustic Emission Feature Extraction and Classification for Rail Crack Monitoring

机译：轨道裂纹监测的声发射特征提取与分类

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Crack monitoring of rails aims to identify fatigue cracks in advance in order to ensure a safe and smooth operation of railway system. This study focuses on the rail crack monitoring using acoustic emission (AE) technique in the railway field typically with complex crack conditions and high operational noise. There are mainly three types of AE waves respectively induced by operational noise, crack propagation and impact, which need to be carefully distinguished from each other for the sake of quantitative crack monitoring. Wavelet transform (WT) was applied to represent the features of AE waves in the time-frequency domain. AE waves induced by different mechanisms were found to contain various instantaneous frequency components. A deep convolutional neural network (CNN) with transfer learning was proposed as an automatic feature extractor and classifier to evaluate the WT plots of AE waves. The CNN was trained, validated and tested using AE data collected through field and laboratory tests. The results demonstrated that the proposed methodology performed well in classifying AE waves induced by different mechanisms in the railway field.
机译：铁路裂缝监测旨在提前发现疲劳裂缝，以确保铁路系统安全，平稳地运行。这项研究的重点是通常在复杂的裂纹条件和高运行噪音的铁路领域中使用声发射（AE）技术进行的铁路裂纹监测。工作噪声，裂纹扩展和冲击分别引起三种类型的AE波，为了定量监测裂纹，需要仔细区分它们。应用小波变换（WT）表示时频域中的AE波的特征。发现由不同机制引起的AE波包含各种瞬时频率分量。提出了一种具有转移学习的深度卷积神经网络（CNN）作为自动特征提取器和分类器，以评估AE波的WT图。使用通过现场和实验室测试收集的AE数据对CNN进行了培训，验证和测试。结果表明，所提出的方法在铁路领域中由不同机制引起的声发射波分类中表现良好。

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