Abstract—

Nondestructive testing of rails is regularly conducted using various approaches and methods, including magnetic and eddy current testing methods, to ensure railroad safety. Automatic analysis of large data arrays (defectograms) from the corresponding equipment is an important problem. The analysis is the process of detecting defective pieces and identifying structural elements of a railroad track using defectograms. This article considers the problem of recognizing patterns of structural elements of railroad rails in defectograms of multichannel magnetic and eddy current defectoscopes. Three classes of structural elements of a railroad track are investigated: (1) a bolted joint with a straight or beveled rail connection, (2) a flash butt rail weld, and (3) an aluminothermic rail weld. Patterns that cannot be assigned to these three classes are conventionally considered as defects and are attributed to a separate fourth class. Patterns of structural elements are recognized in defectograms using a neural network based on the TensorFlow open library. For this purpose, each defectogram area selected for analysis is converted into a grayscale image with a size of 20 by 39 pixels.

中文翻译：

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神经网络在磁涡流缺陷图中识别钢轨结构元素的应用

摘要-

定期使用各种方法和方法（包括磁和涡流测试方法）对铁轨进行无损检测，以确保铁路安全。来自相应设备的大型数据阵列（缺陷图）的自动分析是一个重要的问题。分析是检测缺陷零件并使用缺陷图识别铁轨结构元素的过程。本文考虑了在多通道磁涡流探伤仪的缺陷图中识别铁轨结构元素的模式的问题。研究了铁轨的三类结构元素：（1）具有笔直或倾斜轨道连接的螺栓连接；（2）闪光对接焊缝；（3）铝热轨焊缝。不能分配给这三类的图案通常被认为是缺陷，并且被归因于单独的第四类。使用基于TensorFlow开放库的神经网络在缺陷图中识别结构元素的图案。为此，将选择进行分析的每个缺陷图区域转换为大小为20 x 39像素的灰度图像。