Author: Aleksei Kaliuzhnyi

To ensure the safety of rail traffic, non-destructive testing of rails is regularly carried out using various approaches and methods. One of the main approaches to determining the operational condition of railway rails is ultrasonic non-destructive testing. The assessment of the test results depends on the defectoscopist. The need to reduce the workload on humans and improve the efficiency of the process of analyzing ultrasonic testing data makes the task of creating an automated system relevant. The purpose of this work is to evaluate the possibility of creating an effective system for recognizing rail defects from ultrasonic inspection defectograms using ML methods.

Domain Analysis

The railway track consists of rail sections connected together by bolts and welded joints. When a defectoscope device equipped with generating piezoelectric transducers (PZTs) passes along the railway track, ultrasonic pulses are emitted into the rail at a predetermined frequency. The receiving PZTs then register the reflected waves. The detectability of defects by the ultrasonic method is based on the principle of reflection of waves from inhomogeneities in the metal since cracks, including other inhomogeneities, differ in their acoustic resistance from the rest of the metal.

To ensure traffic safety in railway transport, non-destructive inspection of rails is regularly carried out using various approaches and methods. One of the main approaches to determining the operational condition of railway rails is ultrasonic non-destructive testing [1]. Currently, the search for images of rail defects using the received flaw patterns is performed by a human. The successful development of algorithms for searching and classifying data makes it possible to propose the use of machine learning methods to identify rail defects and reduce the workload on humans by creating expert systems.

The complexity of creating such systems is described in [1, 3-6, 22] and is due, on the one hand, to the variety of graphic images obtained during multi-channel ultrasonic inspection of rails, and on the other hand, to the small number of data copies with defects (not balanced). One of the possible ways to create expert systems in this area is an approach based on the decomposition of the complex task of analyzing the entire multichannel defectogram into individual channels or their sets, characterizing individual types of defects.