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A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation

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  • Davila-Frias, Alex
  • Yodo, Nita
  • Le, Trung
  • Yadav, Om Prakash

Abstract

Most research on network reliability has considered links to have a binary state, i.e., functioning or failed, whereas nodes are considered flawless. In a more realistic scenario, both links and nodes might fail or may exhibit degradation behavior before failing. This study develops a framework to estimate the all-terminal reliability of a network that considers the degradation and probability of failure of all nodes and links in a network. Unlike previous works on network reliability that considered constant reliability for links, this paper considers the reliability of links, nodes, and the network as functions of time. In the proposed framework, the Bayesian methods (BM) are employed to estimate the reliability of links and nodes as functions of time considering degradation data. Due to the complexity of the all-terminal reliability problem, and to get fast estimations of the reliability of a network, an integration of Monte Carlo (MC) and Deep Neural Networks (DNNs) is proposed. The proposed MC algorithm can estimate the network reliability for given nodes and links reliability values. To speed up the calculation, a DNN model is integrated into the framework, thus enabling accurate and fast estimation of network reliability for given link and node reliability values. The DNN accuracy, based on the RMSE (0.01460), outperforms previous traditional artificial neural network (ANN) approaches. Moreover, the DNN model takes 0.3 ms to compute the reliability for any given links and reliability values. The proposed framework can provide not only reliability point estimates but also credible intervals. Finally, we take advantage of Bayesian methods to integrate new data into the framework as they become available. The framework uses the new data to refine and further update the degradation model parameters and the prediction of the reliability of links, nodes, and the network. The proposed methodology has been demonstrated with the real-world network topology Ion (125 nodes, 150 links) with actual degradation data.

Suggested Citation

  • Davila-Frias, Alex & Yodo, Nita & Le, Trung & Yadav, Om Prakash, 2023. "A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004987
    DOI: 10.1016/j.ress.2022.108881
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    References listed on IDEAS

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    1. Ramirez-Marquez, José Emmanuel & Rocco, Claudio M., 2008. "All-terminal network reliability optimization via probabilistic solution discovery," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1689-1697.
    2. Jia Liu & Daniel J. Nordman & William Q. Meeker, 2016. "The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 275-284, July.
    3. Kawahara, Jun & Sonoda, Koki & Inoue, Takeru & Kasahara, Shoji, 2019. "Efficient construction of binary decision diagrams for network reliability with imperfect vertices," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 142-154.
    4. Yeh, Wei-Chang, 2021. "Novel Algorithm for Computing All-Pairs Homogeneity-Arc Binary-State Undirected Network Reliability," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Yeh, Wei-Chang, 2021. "Novel binary-addition tree algorithm (BAT) for binary-state network reliability problem," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Yeh, Wei-Chang, 2021. "A quick BAT for evaluating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Chang, Ping-Chen & Huang, Ding-Hsiang & Lin, Yi-Kuei & Nguyen, Thi-Phuong, 2021. "Reliability and maintenance models for a time-related multi-state flow network via d-MC approach," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    9. Alkaff, Abdullah & Qomarudin, Mochamad Nur & Bilfaqih, Yusuf, 2021. "Network reliability analysis: matrix-exponential approach," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    10. George S. Fishman, 1986. "A Monte Carlo Sampling Plan for Estimating Network Reliability," Operations Research, INFORMS, vol. 34(4), pages 581-594, August.
    11. Li, Daqing & Zhang, Qiong & Zio, Enrico & Havlin, Shlomo & Kang, Rui, 2015. "Network reliability analysis based on percolation theory," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 556-562.
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    5. Yasir Mahmood & Tanzina Afrin & Ying Huang & Nita Yodo, 2023. "Sustainable Development for Oil and Gas Infrastructure from Risk, Reliability, and Resilience Perspectives," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    6. Yu, Shui & Ren, Yuyao & Wu, Xiao & Guo, Peng & Li, Yun, 2024. "Dynamic pruning-based Bayesian support vector regression for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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