Abstract The focus of this study is on locating surface and sub-surface defects that occur in rail tracks using Rayleigh waves that were emitted and sensed by a fully non-contact laser transduction system. As the quality of received signals varies with respect to the rail surface characteristics, spotting the reflection from a defect can be extremely challenging. These signals are in general contaminated with noise and have low repeatability that could hinder the proper identification of the defect signal. In view of this, an innovative signal processing technique called a self-adaptive-smart algorithm (SASA) was designed and developed. In SASA, the incident wave that is the first coming wave-packet is taken as a mother wavelet. A library of possible mother wavelets was designed based on the experimental data. As the incident wave for each sensing point varies because of the physical condition of the rail surface and the laser excitation, the algorithm automatically picks the mother wavelet from the library that generates the largest absolute cross-correlation with the incident wave. SASA is found to be able to suppress the unwanted wave packets from the overall signal leaving behind only the incident wave for a healthy specimen, and the incident wave and its reflection from the defect for a damaged specimen. The functioning of the algorithm was successfully tested by carrying out extensive experiments on a real rail track in the presence of different types of surface and sub-surface defects on its head and web. The obtained results prove the effectiveness of using SASA in localizing defects in rails with a marginal error. Notably, the proposed method has benefits such as being self-adaptive, can help suppress high levels of noise, bring the peak of defect reflected wave in the center, and distinguish between a healthy and damaged sample.

中文翻译：

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设计一种创新的自适应智能算法，使用完全非接触式激光转换系统发射和感测的瑞利波来研究铁路轨道的结构完整性

摘要 本研究的重点是使用由完全非接触式激光转换系统发射和感测的瑞利波来定位轨道中发生的表面和亚表面缺陷。由于接收信号的质量随轨道表面特性而变化，因此发现缺陷的反射可能极具挑战性。这些信号通常被噪声污染，并且具有低可重复性，可能会妨碍对缺陷信号的正确识别。有鉴于此，设计并开发了一种称为自适应智能算法（SASA）的创新信号处理技术。在 SASA 中，作为第一个到来的波包的入射波被作为母小波。根据实验数据设计了一个可能的母小波库。由于每个传感点的入射波因轨道表面的物理条件和激光激发而变化，算法会自动从库中选择与入射波产生最大绝对互相关的母小波。发现 SASA 能够抑制整体信号中不需要的波包，仅留下健康样本的入射波，以及受损样本的入射波及其从缺陷处的反射。该算法的功能通过在实际铁轨上进行大量实验而成功测试，该铁轨的头部和腹板存在不同类型的表面和亚表面缺陷。获得的结果证明了使用 SASA 在定位具有边际误差的钢轨缺陷方面的有效性。尤其，