Application of Machine Learning Methods to Control the Process of Defectoscopy of Railway Tracks

This article describes the application of machine learning methods to control the process of railway flaw detection. It is told about the new capabilities of computer technology, which make it possible to clarify the damage for the lineman. A new way of interaction of a specialist with diagnostic in...

Full description

Saved in:
Bibliographic Details
Published in2021 IV International Conference on Control in Technical Systems (CTS) pp. 64 - 67
Main Authors Subbotin, Alexey N., Zhdanov, Vladimir S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.09.2021
Subjects
Cloud computing
foggy computing environments
Internet of Things
Machine learning
non-destructive testing of railway tracks
Process control
Rail transportation
Rails
Task analysis
ultrasound diagnostics
video processing
Virtual machining
Online AccessGet full text

Cover

More Information
Summary:This article describes the application of machine learning methods to control the process of railway flaw detection. It is told about the new capabilities of computer technology, which make it possible to clarify the damage for the lineman. A new way of interaction of a specialist with diagnostic information using the Internet of Things is proposed. The description of architecture is given according to three standards at the design stage. An application for a smartphone has been developed. The effectiveness of using foggy computing environments and cloud technologies for recognizing damage to railway tracks using machine learning has been proven. Specific examples are given.
DOI:10.1109/CTS53513.2021.9562911