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Multi-state system modeling and reliability assessment for groups of high-speed train wheels
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Chi, Zhexiang, Chen, Ruoran, Huang, Simin, Li, Yan-Fu, Zhou, Bin and Zhang, Wenjuan (2020) Multi-state system modeling and reliability assessment for groups of high-speed train wheels. Reliability Engineering & System Safety, 202 . 107026. doi:10.1016/j.ress.2020.107026 ISSN 0951-8320.
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Official URL: http://dx.doi.org/10.1016/j.ress.2020.107026
Abstract
The polygonization of high-speed train wheels is commonly considered as a serious threat to reliability of high-speed trains all over the world. Traditional studies in polygonization of wheels focus on modeling its mechanism via physics-based models. However, to the knowledge of the authors, the polygonization process has not been fully explained by existing studies due to the complex degradation mechanism and varying operation environment which is difficult to monitor and measure. In this article, the data-driven multi-state system (MSS) models are developed for the reliability assessment of high-speed train wheels based on the analysis of actual operation data. The results can be used to support practical maintenance decision making. Another contribution of this paper is to prove that the environmental factors exert significant impacts on the reliability of Chinese high-speed train wheels. This finding has been included in the development of the MSS models. The results by these models are used to suggest a more reasonable maintenance schedule for Chinese high-speed train wheels. The obtained maintenance intervals are expected to assist the railway companies to reduce the operational risk as well as improve the maintenance efficiency.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Social Sciences > Warwick Business School > Operations Management Faculty of Social Sciences > Warwick Business School |
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Journal or Publication Title: | Reliability Engineering & System Safety | ||||||||
Publisher: | Elsevier Ltd. | ||||||||
ISSN: | 0951-8320 | ||||||||
Official Date: | October 2020 | ||||||||
Dates: |
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Volume: | 202 | ||||||||
Article Number: | 107026 | ||||||||
DOI: | 10.1016/j.ress.2020.107026 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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