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Detection and classification of rail track flaws using inertial and magnetometric sensors

Authors: Alexander M. Boronakhin; Daniil Yu. Larionov; Liudmila N. Podgornaya; Anna N. Tkachenko; Roman V. Shalymov;

Detection and classification of rail track flaws using inertial and magnetometric sensors

Abstract

The team of the Department of Laser Measuring and Navigation Systems of SPbGETU LETI proposed a method for diagnosing surface defects based on inertial measurements. This approach was later introduced into the AVIKON-03M flaw detector car produced by the JSC “Radioavionika”, which was equipped with a set of micromechanical inertial modules for this purpose. The system operation confirmed the possibility of measuring the surface defects' parameters. However, during the analysis of the inertial data it was very problematic to distinguish the signals received when moving along a rail defect from those received while passing a rail joint. That raised the question of identifying and classifying the detected defects. For this purpose, it is now proposed to perform data fusion of measurements obtained from inertial sensors and a magnetometric subsystem installed on the car. This data fusion is the subject of this work.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Top 10%
Top 10%
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