Switch Rail Reduction Value Deviation’s Impact on Wheel–Rail Dynamic Interaction and Its Efficient Identification Method: A Numerical and Experimental Study

Railway turnout is a critical railway infrastructure that guides trains in switching tracks. Over time, uneven rail wear can lead to switch rail reduction value (SRRV) deviation, a typical structural defect that compromises turnout functionality and jeopardizes train operation safety. Current SRRV deviation detection methods rely primarily on inefficient manual inspections, making it difficult to ensure operational safety. To address this issue, the study carried out a comprehensive investigation combining numerical and experimental analyses. First, a rigid–flexible coupled dynamics model of a vehicle-turnout system was developed to analyze the wheel–rail dynamic interaction forces and contact relationships under various SRRV deviation conditions. The results revealed that SRRV deviation significantly affects both wheel–rail interaction forces and the turnout structural irregularity wavelength. Thus, based on discrete wavelet transform (DWT), a wheel–rail force trend component was derived that can effectively analyze the turnout structural irregular wavelength, and the mapping relationship between SRRV deviation and the wheel–rail force trend component was then established. Finally, an efficient and accurate method for identifying SRRV deviation based on wheel–rail force trend component was proposed and validated using field-measured data from trains passing through turnouts. This study contributes to the timely detection of track defects, helping to prevent safety incidents during train operations.

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Publication Details

Journal
Applied Sciences
Published
2024-12-23
DOI
https://doi.org/10.3390/app142412047
Citations
3
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
0.64

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article

Switch Rail Reduction Value Deviation’s Impact on Wheel–Rail Dynamic Interaction and Its Efficient Identification Method: A Numerical and Experimental Study

Qiantao Ma, Ji Liu, Pu Wang
3 citations
Applied Sciences
Railway Engineering and Dynamics
0.64
article

Switch Rail Reduction Value Deviation’s Impact on Wheel–Rail Dynamic Interaction and Its Efficient Identification Method: A Numerical and Experimental Study

Qiantao Ma, Ji Liu, Pu Wang
article en
3 citations

Abstract

Railway turnout is a critical railway infrastructure that guides trains in switching tracks. Over time, uneven rail wear can lead to switch rail reduction value (SRRV) deviation, a typical structural defect that compromises turnout functionality and jeopardizes train operation safety. Current SRRV deviation detection methods rely primarily on inefficient manual inspections, making it difficult to ensure operational safety. To address this issue, the study carried out a comprehensive investigation combining numerical and experimental analyses. First, a rigid–flexible coupled dynamics model of a vehicle-turnout system was developed to analyze the wheel–rail dynamic interaction forces and contact relationships under various SRRV deviation conditions. The results revealed that SRRV deviation significantly affects both wheel–rail interaction forces and the turnout structural irregularity wavelength. Thus, based on discrete wavelet transform (DWT), a wheel–rail force trend component was derived that can effectively analyze the turnout structural irregular wavelength, and the mapping relationship between SRRV deviation and the wheel–rail force trend component was then established. Finally, an efficient and accurate method for identifying SRRV deviation based on wheel–rail force trend component was proposed and validated using field-measured data from trains passing through turnouts. This study contributes to the timely detection of track defects, helping to prevent safety incidents during train operations.

Applied SciencesVol. 14(24)
China Railway Corporation (CN), China Academy of Railway Sciences (CN), Southwest Jiaotong University (CN)
National Natural Science Foundation of China, China Academy of Railway Sciences
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Railway Engineering and Dynamics
0.64
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