Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
Source abstract
Abstract
The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Railway Engineering and Dynamics · Structural Health Monitoring Techniques · Geotechnical Engineering and Underground Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CONSTRUCT-LESE, Faculty of Engineering (FEUP), University of Porto, 4200-465 Porto, Portugal
Funding
This work was financially supported by: Base Funding - UIDB/04708/2020 and Programmatic Funding - UIDP/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC) · The paper reflects research developed in the ambit of the project PEDDIR DEMO, NORTE-01-0247-FEDER-006397, founded by Agência Nacional de Inovação S.A., program P2020|COMPETE - Projetos Demonstradores em Copromoção. · The authors are also sincerely grateful to the European Commission for the financial sponsorship of H2020 MARIE SKŁODOWSKA-CURIE RISE Project, Grant No. 691135 “RISEN: Rail Infrastructure Systems Engineering Network,” · project IN2TRACK2 – Research into enhanced track and switch and crossing system 2, funded by European funds through the H2020 (SHIFT2RAIL Innovation Programme).
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