The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted-probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy-haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality-based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy-haul railways.

中文翻译：

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使用新颖的多维尺度聚类方法进行钢轨轮廓数据的磨损诊断


铁路系统故障诊断对于其舒适性、高效性和安全性具有重要意义。在考虑钢轨健康状况时，钢轨表面磨损是关键影响因素。本文通过使用基于新型信息相异性度量（IDM）的多维尺度来聚类完整和不同磨损的钢轨轮廓数据，完成非接触式钢轨磨损诊断。 IDM利用色散模式的加权概率分布从钢轨轮廓数据中提取准确的时域特征，并且最大程度地减少信息丢失，可以大大提高磨损诊断的准确性。真实实验的所有分析数据均由检查车上的激光扫描仪摄像头收集，检查车上具有不同类型表面磨损的重载铁路钢轨。模拟和基于实际数据的实验结果表明，与现有方法相比，所提出的方法可以识别磨损轮廓数据并更有效地区分不同类型的磨损轮廓。因此，该方法为重载铁路钢轨表面磨损的诊断提供了新的思路。