IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v202y2020ics0951832020305275.html
   My bibliography  Save this article

Multi-State System Modeling and Reliability Assessment for Groups of High-speed Train Wheels

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020305275
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2020.107026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tian, Tianzi & Yang, Jun & Li, Lei & Wang, Ning, 2023. "Reliability assessment of performance-based balanced systems with rebalancing mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Zeng, Zhiguo & Fang, Yi-Ping & Zhai, Qingqing & Du, Shijia, 2021. "A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    3. Fang, Chen & Cui, Lirong, 2021. "Balanced Systems by Considering Multi-state Competing Risks Under Degradation Processes," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Cao, Yingsai & Liu, Sifeng & Fang, Zhigeng & Dong, Wenjie, 2020. "Modeling ageing effects for multi-state systems with multiple components subject to competing and dependent failure processes," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Wu, Bei & Cui, Lirong & Fang, Chen, 2020. "Multi-state balanced systems with multiple failure criteria," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    6. Zhang, Hanxiao & Li, Yan-Fu, 2022. "Robust optimization on redundancy allocation problems in multi-state and continuous-state series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2021. "Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    10. Fang, Jiayue & Kang, Rui & Chen, Ying, 2021. "Reliability evaluation of non-repairable systems with failure mechanism trigger effect," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    11. Zhai, Qingqing & Chen, Piao & Hong, Lanqing & Shen, Lijuan, 2018. "A random-effects Wiener degradation model based on accelerated failure time," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 94-103.
    12. Peng, Rui & Xiao, Hui & Liu, Hanlin, 2017. "Reliability of multi-state systems with a performance sharing group of limited size," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 164-170.
    13. Liu, Zhitao & Tan, CherMing & Leng, Feng, 2015. "A reliability-based design concept for lithium-ion battery pack in electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 169-177.
    14. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    15. Wang, Guanjun & Duan, Fengjun & Zhou, Yifan, 2018. "Reliability evaluation of multi-state series systems with performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 58-63.
    16. Peng, Rui & Mo, Huadong & Xie, Min & Levitin, Gregory, 2013. "Optimal structure of multi-state systems with multi-fault coverage," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 18-25.
    17. Chatenet, Q. & Remy, E. & Gagnon, M. & Fouladirad, M. & Tahan, A.S., 2021. "Modeling cavitation erosion using non-homogeneous gamma process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    18. Xia, Weifu & Wang, Yanhui & Hao, Yucheng & He, Zhichao & Yan, Kai & Zhao, Fan, 2024. "Reliability analysis for complex electromechanical multi-state systems utilizing universal generating function techniques," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    19. Eryilmaz, Serkan & Navarro, Jorge, 2022. "A decision theoretic framework for reliability-based optimal wind turbine selection," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    20. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305275. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.