IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v231y2023ics0951832022006317.html
   My bibliography  Save this article

Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model

Author

Listed:
  • Wang, Jian
  • Gao, Shibin
  • Yu, Long
  • Zhang, Dongkai
  • Xie, Chenlin
  • Chen, Ke
  • Kou, Lei

Abstract

Lightning-related failures are of great concerns for the reliable performance of overhead contact lines (OCLs) of high-speed railway. Predicting lightning-related failure probability is valuable to capture the recurrence of OCL failures due to lightning strike and enable predictive maintenance decision-making. In this paper, a data-driven Bayesian network (BN) approach with spatiotemporal fragility model is developed to investigate the dependencies between lightning strike and OCL failures, and predict lightning-related failure risk of OCLs. It consists of three critical components, (1) a probabilistic lightning model that integrates multiple key lightning parameters is proposed to capture the uncertainty in the occurrence and intensity of lightning strike; (2) a spatiotemporal fragility model of OCL corridor is presented to examine the impacts of lightning strike on OCL failure probability; (3) furthermore, the Bayesian network is embedded with above-mentioned two models to predict lightning-related failure risk of OCLs, improving its robustness. Compared with other advanced prediction methods, the proposed approach achieves better prediction performance with high accuracy over imbalanced dataset. In addition, it can still work acceptably on noisy lightning data with a signal-to-noise ratio of 15dB or higher.

Suggested Citation

  • Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Xie, Chenlin & Chen, Ke & Kou, Lei, 2023. "Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006317
    DOI: 10.1016/j.ress.2022.109016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022006317
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.109016?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. Nofal, Omar M. & van de Lindt, John W. & Do, Trung Q., 2020. "Multi-variate and single-variable flood fragility and loss approaches for buildings," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Liu, Haibin & Davidson, Rachel A. & Apanasovich, Tatiyana V., 2008. "Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 897-912.
    3. Necci, Amos & Argenti, Francesca & Landucci, Gabriele & Cozzani, Valerio, 2014. "Accident scenarios triggered by lightning strike on atmospheric storage tanks," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 30-46.
    4. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Ding, Chugang & Chen, Ke & Kou, Lei, 2022. "Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Qin, Hao & Stewart, Mark G., 2020. "Construction defects and wind fragility assessment for metal roof failure: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    7. Zhu, Wenjin & Castanier, Bruno & Bettayeb, Belgacem, 2019. "A dynamic programming-based maintenance model of offshore wind turbine considering logistic delay and weather condition," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    8. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    9. Mukherjee, Sayanti & Nateghi, Roshanak & Hastak, Makarand, 2018. "A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 283-305.
    10. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Argyroudis, Sotirios A. & Mitoulis, Stergios Α. & Winter, Mike G. & Kaynia, Amir M., 2019. "Fragility of transport assets exposed to multiple hazards: State-of-the-art review toward infrastructural resilience," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    12. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    13. Necci, Amos & Antonioni, Giacomo & Cozzani, Valerio & Krausmann, Elisabeth & Borghetti, Alberto & Nucci, Carlo Alberto, 2014. "Assessment of lightning impact frequency for process equipment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 95-105.
    14. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. Zhang, Yanping & Cai, Baoping & Liu, Yiliu & Jiang, Qiangqiang & Li, Wenchao & Feng, Qiang & Liu, Yonghong & Liu, Guijie, 2021. "Resilience assessment approach of mechanical structure combining finite element models and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Liu, Jin & Zhai, Changhai & Yu, Peng, 2022. "A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Rahman, Md Samsur & Colbourne, Bruce & Khan, Faisal, 2021. "Risk-Based Cost Benefit Analysis of Offshore Resource Centre to Support Remote Offshore Operations in Harsh Environment," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    18. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Sarajcev, P. & Jakus, D. & Mudnic, E., 2020. "Gaussian process regression modeling of wind turbines lightning incidence with LLS information," Renewable Energy, Elsevier, vol. 146(C), pages 1221-1231.
    20. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    21. Necci, Amos & Antonioni, Giacomo & Cozzani, Valerio & Krausmann, Elisabeth & Borghetti, Alberto & Alberto Nucci, Carlo, 2013. "A model for process equipment damage probability assessment due to lightning," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 91-99.
    22. Ma, Liyang & Christou, Vasileios & Bocchini, Paolo, 2022. "Framework for probabilistic simulation of power transmission network performance under hurricanes," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    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. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Wang, Jian & Gao, Shibin & Yu, Long & Ma, Chaoqun & Zhang, Dongkai & Kou, Lei, 2023. "A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 235(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. Wang, Jian & Gao, Shibin & Yu, Long & Ma, Chaoqun & Zhang, Dongkai & Kou, Lei, 2023. "A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Ding, Chugang & Chen, Ke & Kou, Lei, 2022. "Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of risk modification due to safety barrier performance degradation in Natech events," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of safety barrier performance in the mitigation of domino scenarios caused by Natech events," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Necci, Amos & Antonioni, Giacomo & Bonvicini, Sarah & Cozzani, Valerio, 2016. "Quantitative assessment of risk due to major accidents triggered by lightning," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 60-72.
    6. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Misuri, Alessio & Ricci, Federica & Sorichetti, Riccardo & Cozzani, Valerio, 2023. "The Effect of Safety Barrier Degradation on the Severity of Primary Natech Scenarios," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    8. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    9. Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    10. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Argyroudis, Sotirios A. & Mitoulis, Stergios Aristoteles, 2021. "Vulnerability of bridges to individual and multiple hazards- floods and earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    12. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Yunfeng Yang & Guohua Chen & Yuanfei Zhao, 2023. "A Quantitative Framework for Propagation Paths of Natech Domino Effects in Chemical Industrial Parks: Part II—Risk Assessment and Mitigation System," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    14. Yang, Yunfeng & Chen, Guohua & Reniers, Genserik, 2020. "Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    15. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    16. Jasiūnas, Justinas & Heikkinen, Tatu & Lund, Peter D. & Láng-Ritter, Ilona, 2023. "Resilience of electric grid to extreme wind: Considering local details at national scale," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    17. Xue, Gang & Liu, Shifeng & Ren, Long & Gong, Daqing, 2024. "Risk assessment of utility tunnels through risk interaction-based deep learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    18. Weiliang Qiao & Enze Huang & Hongtongyang Guo & Yang Liu & Xiaoxue Ma, 2022. "Barriers Involved in the Safety Management Systems: A Systematic Review of Literature," IJERPH, MDPI, vol. 19(15), pages 1-35, August.
    19. Gangwal, Utkarsh & Dong, Shangjia, 2022. "Critical facility accessibility rapid failure early-warning detection and redundancy mapping in urban flooding," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    20. Yang, Zhen & Dong, Xiaobin & Guo, Li, 2023. "Scenario inference model of urban metro system cascading failure under extreme rainfall conditions," Reliability Engineering and System Safety, Elsevier, vol. 229(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:231:y:2023:i:c:s0951832022006317. 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.