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Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train
Department of Industrial Engineering, Tsinghua University, Beijing, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
Department of Industrial Engineering, Tsinghua University, Beijing, China.
Department of Industrial Engineering, Tsinghua University, Beijing, China.
2020 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 149, article id 107022Article in journal (Refereed) Published
Abstract [en]

Environmental factors, like seasonality, have been proved to exert significant impact on reliability of China high-speed rail train wheels in this article. Most studies on polygonization of train wheels are based on physical models, mathematical models or simulation systems. Normally, characteristics and mechanisms of wheel polygonization are studied under ideal conditions without considering the impact of the environment. However, in practical use, there are many irregular wear wheels and irregular wear cannot be explained by theoretical models with assumptions of ideal conditions. We look at two possible factors in polygonization: seasonality and proximity to engines. Our analysis of field data shows the environmental factor has more impact on wheel polygonization than the mechanical factor. Based on the Bayesian models, the mean time to failure of the wheels under different operation conditions is conducted. A case study of China’s HSR train wheels is conducted to confirm the finding. The case study shows the degree of polygonal wear is much more severe in summer than other seasons. The finding may give a totally new research perspective on polygonization of train wheels. We use Bayesian analysis because this method is useful for small and incomplete data sets. We propose three Bayesian data-driven models.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 149, article id 107022
Keywords [en]
railway safety, prognostics and health management, mean time to failure, Bayesian methods, polygonization, wheel-sets
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-75933DOI: 10.1016/j.measurement.2019.107022ISI: 000490131400013Scopus ID: 2-s2.0-85072207003OAI: oai:DiVA.org:ltu-75933DiVA, id: diva2:1349854
Note

Validerad;2019;Nivå 2;2019-09-23 (johcin)

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-11-06Bibliographically approved

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Lin, Jing

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