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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
  • Zhang, Wenjuan

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.

Suggested Citation

  • Chi, Zhexiang & Chen, Ruoran & Huang, Simin & Li, Yan-Fu & Zhou, Bin & Zhang, Wenjuan, 2020. "Multi-State System Modeling and Reliability Assessment for Groups of High-speed Train Wheels," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305275
    DOI: 10.1016/j.ress.2020.107026
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    References listed on IDEAS

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    1. Alotaibi, Majed A. & Salama, M.M.A., 2016. "An efficient probabilistic-chronological matching modeling for DG planning and reliability assessment in power distribution systems," Renewable Energy, Elsevier, vol. 99(C), pages 158-169.
    2. Li, Yan-Fu & Zio, Enrico, 2012. "A multi-state model for the reliability assessment of a distributed generation system via universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 28-36.
    3. Qiu, Siqi & Ming, Henry X.G., 2019. "Reliability evaluation of multi-state series-parallel systems with common bus performance sharing considering transmission loss," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 406-415.
    4. Levitin, Gregory & Jia, Heping & Ding, Yi & Song, Yonghua & Dai, Yuanshun, 2017. "Reliability of multi-state systems with free access to repairable standby elements," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 192-197.
    5. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui, 2017. "Modeling dependent competing failure processes with degradation-shock dependence," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 422-430.
    6. Zhibing Xu & Yili Hong & Ran Jin, 2016. "Nonlinear general path models for degradation data with dynamic covariates," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(2), pages 153-167, March.
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    Cited by:

    1. Shangguan, Anqi & Xie, Guo & Fei, Rong & Mu, Lingxia & Hei, Xinhong, 2023. "Train wheel degradation generation and prediction based on the time series generation adversarial network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Liu, Jie & Xu, Yubo & Wang, Lisong, 2022. "Fault information mining with causal network for railway transportation system," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Dai, Xinliang & Qu, Sheng & Sui, Hao & Wu, Pingbo, 2022. "Reliability modelling of wheel wear deterioration using conditional bivariate gamma processes and Bayesian hierarchical models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Yuanchen Zeng & Dongli Song & Weihua Zhang & Bin Zhou & Mingyuan Xie & Xiaoyue Qi, 2021. "Risk assessment of wheel polygonization on high-speed trains based on Bayesian networks," Journal of Risk and Reliability, , vol. 235(2), pages 182-192, April.
    5. Abba, Badamasi & Wang, Hong & Bakouch, Hassan S., 2022. "A reliability and survival model for one and two failure modes system with applications to complete and censored datasets," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Men, Tianli & Li, Yan-Fu & Ji, Yujun & Zhang, Xinliang & Liu, Pengfei, 2022. "Health assessment of high-speed train wheels based on group-profile data," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Lin, Shuai & Jia, Limin & Zhang, Hengrun & Zhang, Pengzhu, 2022. "Reliability of high-speed electric multiple units in terms of the expanded multi-state flow network," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Oliveira, Ricardo P. & Achcar, Jorge A. & Mazucheli, Josmar & Bertoli, Wesley, 2021. "A new class of bivariate Lindley distributions based on stress and shock models and some of their reliability properties," Reliability Engineering and System Safety, Elsevier, vol. 211(C).

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