Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Collected scientific...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

ANALYSIS OF DAMAGE TO THE RUNNING GEAR OF UNIVERSAL GONDOLA CARS

ANALYSIS OF DAMAGE TO THE RUNNING GEAR OF UNIVERSAL GONDOLA CARS

Abstract

This article presents an in-depth statistical analysis of failure occurrences in the running gear of universal freight gondola cars operated by Ukrainian Railways. Drawing from a dataset of over 10,000 inspected gondola cars across eight models over the period 2021–2024, the study identifies key trends in fault distribution by model, type, and frequency. The focus is placed on typical failures such as spring fractures, wheel flange wear, axle fatigue, and overheating of journal bearings. A defect matrix is developed to cross-reference failure modes with specific wagon types. The analysis shows that models 12-9046 and 12-132 account for the highest number of failures, likely due to their wide deployment and specific design features. The study also reveals that cars with more than 25 years of service demonstrate significantly higher failure rates compared to newer models, indicating the importance of life-cycle-based diagnostics. Statistical tools such as classification grouping and Pearson’s chi-squared test were used to verify dependencies between wagon models and failure types. The results confirm a strong correlation, emphasizing the need for model-specific maintenance strategies. Furthermore, failure intensity was calculated for each wagon type, demonstrating that even newer designs with improved structures (e.g., models 12-1704-04 and 12- 296-01) are not immune to specific technical issues. The conclusions include recommendations for technical maintenance planning, highlighting the benefits of targeted diagnostics and predictive repair scheduling. The outcomes of this research may be applied to enhance operational safety, reduce downtime, and improve the economic efficiency of wagon maintenance. The study contributes to the broader field of rolling stock reliability engineering and supports the modernization of freight fleet management practices based on empirical data and statistical modeling.

У статті подано статистичний аналіз несправностей ходових частин вантажних напіввагонів. Виділено моделі, що мають підвищену інтенсивність відмов, визначено домінуючі типи дефектів і оцінено їхній вплив на експлуатаційну надійність рухомого складу. Застосовано методи класифікаційного групування та перевірку статистичної значущості залежностей. Основну увагу приділено аналізу типових несправностей колісних пар і буксових вузлів. Порівняно пошкоджуваність різних моделей напіввагонів і показано, що найчастіше відмовляють напіввагони моделі 12-9046. Встановлено, що найбільш поширеними несправностями є знос гребенів коліс, перегрів підшипників і дефекти елементів автозчіпного обладнання. На підставі отриманих результатів сформульовано рекомендації щодо організації технічного обслуговування.

Keywords

freight car, gondola wagon, running gear, failure analysis, spring defects, axle wear, statistical diagnostics, maintenance optimization, напіввагон, кузов, ходові частини, технічний стан, несправності, інтенсивність відмов, статистичний аналіз

  • BIP!
    Impact byBIP!
    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).
    0
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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!
0
Average
Average
Average
gold