Journal
MEASUREMENT
Volume 149, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107022
Keywords
Railway safety; Prognostics and health management; Mean time to failure; Bayesian methods; Polygonization; Wheel-sets
Funding
- Center for Data-Centric Management in the Department of Industrial Engineering at Tsinghua University
- Lulea Railway Research Center (JVTC) in Sweden
- National Natural Science Foundation of China (NSFC) [71801045]
- China Postdoctoral Science Foundation [2019M650751]
- Youth Innovative Talent Project from the Department of Education of Guangdong Province [2017KQNCX191]
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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. (C) 2019 Elsevier Ltd. All rights reserved.
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