Research on the wheel wear prediction of heavy-haul freight wagon based on the roller rig test and line test

Purpose To reduce the wheel maintenance costs caused by wheel wear and to transition from traditional periodic maintenance to condition-based maintenance for railway freight wagons, it is necessary to investigate the prediction of wheel wear and understand the evolution rule of wheel profile wear. Design/methodology/approach This paper established a wheel wear prediction model for railway freight wagons based on Archard’s wear theory and proposed a prediction method that combines vehicle system dynamic, interpolation iteration and intelligent simulation. The wear coefficients in the model were obtained through wheel wear tests by using the roller rig. The model’s effectiveness was further verified through line testing and simulation models, and the corrected wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Findings The wheel wear prediction showed that the results of the wheel wear prediction model by adopting the wear coefficients obtained from the roller rig tests are close to the actual wheel wear, with the difference of the maximum in wear depth at the nominal rolling circle being within 7%. Originality/value This paper proposed a method that can establish a database of wheel wear coefficients for predicting wheel wear of railway freight wagons under similar operating conditions. The revised wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0329/

Authors

Institutions

Publication Details

Journal
Industrial Lubrication and Tribology
Published
2024-12-16
DOI
https://doi.org/10.1108/ilt-09-2024-0329
Citations
2
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
0.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the wheel wear prediction of heavy-haul freight wagon based on the roller rig test and line test

Yayun Qi, Liangwei Zhang
2 citations
Industrial Lubrication and Tribology
Railway Engineering and Dynamics
0.43
article

Research on the wheel wear prediction of heavy-haul freight wagon based on the roller rig test and line test

Yayun Qi, Liangwei Zhang
article en
2 citations

Abstract

Purpose To reduce the wheel maintenance costs caused by wheel wear and to transition from traditional periodic maintenance to condition-based maintenance for railway freight wagons, it is necessary to investigate the prediction of wheel wear and understand the evolution rule of wheel profile wear. Design/methodology/approach This paper established a wheel wear prediction model for railway freight wagons based on Archard’s wear theory and proposed a prediction method that combines vehicle system dynamic, interpolation iteration and intelligent simulation. The wear coefficients in the model were obtained through wheel wear tests by using the roller rig. The model’s effectiveness was further verified through line testing and simulation models, and the corrected wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Findings The wheel wear prediction showed that the results of the wheel wear prediction model by adopting the wear coefficients obtained from the roller rig tests are close to the actual wheel wear, with the difference of the maximum in wear depth at the nominal rolling circle being within 7%. Originality/value This paper proposed a method that can establish a database of wheel wear coefficients for predicting wheel wear of railway freight wagons under similar operating conditions. The revised wear coefficient can be used for wear prediction of heavy-haul freight wagons in China. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0329/

Industrial Lubrication and TribologyVol. 77(6)
Central South University (CN), CRRC (China) (CN), Chongqing Jiaotong University (CN)
Openalex Percentile: Top 40%
Railway Engineering and Dynamics
0.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on the wheel wear prediction of heavy-haul freight wagon based on the roller rig test and line test — Yayun Qi, Liangwei Zhang · Industrial Lubrication and Tribology (2024) | TGRS Research Map | TGRS