IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v213y2021ics0951832021002775.html
   My bibliography  Save this article

Hybrid remaining useful life prediction method. A case study on railway D-cables

Author

Listed:
  • Zang, Yu
  • Shangguan, Wei
  • Cai, Baigen
  • Wang, Huasheng
  • Pecht, Michael. G.

Abstract

This paper develops a hybrid remaining useful life (RUL) prediction method and explores the feasibility for complex system equipment, using one of transmission equipment D-cables in high-speed railways as an example. RUL prediction is a promising way to reduce high maintenance costs for high-speed railways. However, there is no sufficient actual life-cycle data due to the lack of sensors, and no mature physics-of-failure model of the equipment in high-speed railways, which make it difficult to predict RUL. To solving this problem, firstly the failure modes, mechanisms, and effects of the D-cables are first analyzed, and accelerated life tests are run under different thermal stresses in Ansys to obtain the life-cycle data. Based on the life-cycle data, particle filtering (PF) method predicts the RUL based on Paris-Law model, meanwhile feedforward neural network (FNN) predicts the RUL under the same thermal stress with PF method, finally a hybrid RUL prediction method that combines model-based and data-driven methods is developed. The results are verified using simulation.

Suggested Citation

  • Zang, Yu & Shangguan, Wei & Cai, Baigen & Wang, Huasheng & Pecht, Michael. G., 2021. "Hybrid remaining useful life prediction method. A case study on railway D-cables," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002775
    DOI: 10.1016/j.ress.2021.107746
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021002775
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107746?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. Li, Rui & Verhagen, Wim J.C. & Curran, Richard, 2020. "A systematic methodology for Prognostic and Health Management system architecture definition," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    5. Baptista, Marcia & Henriques, Elsa M.P. & de Medeiros, Ivo P. & Malere, Joao P. & Nascimento, Cairo L. & Prendinger, Helmut, 2019. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 228-239.
    6. Wang, Yiwei & Gogu, Christian & Kim, Nam H. & Haftka, Raphael T. & Binaud, Nicolas & Bes, Christian, 2019. "Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 86-100.
    7. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    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. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Shuto, Susumu & Amemiya, Takashi, 2022. "Sequential Bayesian inference for Weibull distribution parameters with initial hyperparameter optimization for system reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Hachem, Hassan & Vu, Hai Canh & Fouladirad, Mitra, 2024. "Different methods for RUL prediction considering sensor degradation," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    7. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    12. Wang, Chao & Zhu, Tao & Yang, Bing & Yin, Minxuan & Xiao, Shoune & Yang, Guangwu, 2023. "Remaining useful life prediction framework for crack propagation with a case study of railway heavy duty coupler condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Zhou, Hang & Farsi, Maryam & Harrison, Andrew & Parlikad, Ajith Kumar & Brintrup, Alexandra, 2023. "Civil aircraft engine operation life resilient monitoring via usage trajectory mapping on the reliability contour," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(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. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    3. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    6. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. KarabaÄŸ, Oktay & Eruguz, Ayse Sena & Basten, Rob, 2020. "Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    8. Badía, F.G. & Berrade, M.D. & Cha, Ji Hwan & Lee, Hyunju, 2018. "Optimal replacement policy under a general failure and repair model: Minimal versus worse than old repair," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 362-372.
    9. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    10. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    11. Badía, F.G. & Berrade, M.D. & Lee, Hyunju, 2020. "An study of cost effective maintenance policies: Age replacement versus replacement after N minimal repairs," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    12. Cai, Xiao & Li, Naipeng & Xie, Min, 2024. "RUL prediction for two-phase degrading systems considering physical damage observations," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    13. Ece Zeliha Demirci & Joachim Arts & Geert-Jan Van Houtum, 2022. "A restless bandit approach for capacitated condition based maintenance scheduling," DEM Discussion Paper Series 22-01, Department of Economics at the University of Luxembourg.
    14. Ahmed Elsheikh & Soumaya Yacout & Mohamed-Salah Ouali & Yasser Shaban, 2020. "Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 403-415, February.
    15. Dourado, Arinan & Viana, Felipe A.C., 2021. "Early life failures and services of industrial asset fleets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    16. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    17. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    18. Yu Mo & Liang Li & Biqing Huang & Xiu Li, 2023. "Few-shot RUL estimation based on model-agnostic meta-learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2359-2372, June.
    19. Sánchez, Luciano & Costa, Nahuel & Couso, Inés, 2023. "Simplified models of remaining useful life based on stochastic orderings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).

    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:213:y:2021:i:c:s0951832021002775. 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.