Previously, electronic straightedges with a length of 1 m were widely used to measure the longitudinal profiles of rail joints. However, owing to the lack of an efficient measurement device, rail joints with 3-m wavelengths are seldom studied. In this study, a rail measurement trolley based on the chord-reference method was developed with a measurement wavelength of up to 3 m. A field measurement was performed on a 53-km metro line, and the waveforms of 4340 rail joints were obtained. First, to visualize the distribution of the dataset and to find out the common features, t-distributed stochastic neighbor embedding dimensionality reduction was applied to the rail joint dataset, and each rail joint waveform was mapped to a point in a two-dimensional space. Second, K-means was applied to the rail joint dataset, and six categories of rail joints were obtained. The results indicated that there are two types of rail joints: M-type and W-type, accounting for 18.41% and 76.08% of the total number of joints, respectively, and the remainder are bolted rail joints. Third, to better evaluate rail joint status, the concept of rail joint triangle (RJT) is proposed, and five shape-based features of a rail joint in 3-m wavelength are defined. Finally, using RJT distribution analysis, we observed that the shape-based features provide more essential information about a rail joint, such as symmetry, asymmetry, M-type, or W-type, compared with conventional indexes such as the quality index. Notably, compared with the waveform of a rail joint at 1 m, a 3-m waveform provides significantly more essential information, which can be meaningful for future research on the dynamic impact of rail joints, as well as profile grinding around rail joints. To help other researchers follow our research, our dataset is available on Mendeley Data (RWJ-3 m dataset).

中文翻译：

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基于无监督学习的3米波长钢轨接头轮廓评估

此前，长度为1m的电子直尺被广泛用于测量钢轨接头的纵向轮廓。然而，由于缺乏有效的测量装置，3米波长的钢轨接头的研究很少。本研究开发了一种基于弦参考法的轨道测量小车，测量波长可达3 m。对53公里地铁线路进行了现场测量，获得了4340个钢轨接头的波形。首先，为了可视化数据集的分布并找出共同特征，对钢轨接头数据集应用t分布随机邻域嵌入降维，并将每个钢轨接头波形映射到二维空间中的一个点。二、K-means应用于铁路接头数据集，获得了六类铁路接头。结果表明，钢轨接头有M型和W型两种类型，分别占接头总数的18.41%和76.08%，其余均为螺栓连接钢轨接头。第三，为了更好地评估钢轨接头状态，提出了钢轨接头三角形（RJT）的概念，并定义了3米波长下钢轨接头的五个基于形状的特征。最后，通过 RJT 分布分析，我们观察到与质量指数等传统指标相比，基于形状的特征提供了更多有关钢轨接头的基本信息，例如对称性、不对称性、M 型或 W 型。值得注意的是，与 1 m 处钢轨接头的波形相比，3 m 波形提供了更多重要信息，这对于未来研究钢轨接头的动态影响以及钢轨接头周围的仿形磨削具有重要意义。为了帮助其他研究人员跟进我们的研究，我们的数据集可在 Mendeley Data（RWJ-3 m 数据集）上获取。