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Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines

Author

Listed:
  • Wang, Jian
  • Gao, Shibin
  • Yu, Long
  • Liu, Xingyang
  • Neri, Ferrante
  • Zhang, Dongkai
  • Kou, Lei

Abstract

Overhead contact lines (OCLs) are electric transmission lines that power railways, which are constantly threatened by external weather and environmental factors due to their outdoor location. Hence, for the long-term functioning of railway lines, a weather-driven risk predictor is an essential tool. Current prediction methods mainly adopt a single-point estimation system with fixed weights of neural networks and therefore cannot propagate the uncertainties within the data and model, resulting in unreliable predictions. To enhance safety-risk prevention capabilities, this paper proposes an uncertainty-aware trustworthy weather-driven failure-risk approach for OCLs, in a probabilistic deep multitask learning framework. Firstly, a deep Gaussian process is employed as the backbone model to deal with imbalanced weather-related failure datasets with limited fault samples. Moreover, a multi-task learning framework is embedded to simultaneously predict the multiple weather-driven failure risks under lightning, wind and haze. Finally, the predictive uncertainty is decomposed into epistemic and aleatory uncertainties, where epistemic and aleatory uncertainties account for the uncertainty within the model and data, respectively. Extensive experiments on actual OCLs are carried out to demonstrate the effectiveness of the proposed approach, which can effectively capture the predictive uncertainty and provide trustworthy predictive decisions of mitigating operational risk for railway operators.

Suggested Citation

  • Wang, Jian & Gao, Shibin & Yu, Long & Liu, Xingyang & Neri, Ferrante & Zhang, Dongkai & Kou, Lei, 2024. "Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006488
    DOI: 10.1016/j.ress.2023.109734
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    References listed on IDEAS

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    1. Ye, Yunguang & Huang, Caihong & Zeng, Jing & Wang, Suqin & Liu, Chaotao & Li, Fansong, 2023. "Predicting railway wheel wear by calibrating existing wear models: Principle and application," Reliability Engineering and System Safety, Elsevier, vol. 238(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).
    3. 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).
    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. Guo, Jian & Luo, Cheng & Ma, Kaijiang, 2023. "Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. 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).
    7. Paul R. C. Kent, 2013. "Trustworthy predictions," Nature, Nature, vol. 493(7432), pages 314-315, January.
    8. Bellè, Andrea & Zeng, Zhiguo & Duval, Carole & Sango, Marc & Barros, Anne, 2022. "Modeling and vulnerability analysis of interdependent railway and power networks: Application to British test systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    9. Tao, Longlong & Chen, Liwei & Ge, Daochuan & Yao, Yuantao & Ruan, Fang & Wu, Jie & Yu, Jie, 2022. "An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
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    1. Wang, Jian & Liu, Huiyuan & Gao, Shibin & Yu, Long & Liu, Xingyang & Zhang, Dongkai & Kou, Lei, 2024. "Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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