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Multivariate system reliability analysis considering highly nonlinear and dependent safety events

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  • Sadoughi, Mohammadkazem
  • Li, Meng
  • Hu, Chao

Abstract

Most of the existing system reliability analysis methods have focused on series and parallel systems whose components are weakly nonlinear and dependent. This paper proposes a new system reliability analysis method, named multivariate system reliability analysis (MSRA), for complex engineered systems with highly nonlinear and dependent components that are connected in series, parallel, and mixed configurations. The proposed method first employs multivariate Gaussian process (MGP) to sequentially construct a single surrogate jointly over the performance functions of all components and then performs Monte Carlo simulation on the surrogate model for system reliability analysis. The joint surrogate is updated adaptively using a novel acquisition function named multivariate probability of improvement (MPI). MGP considers the correlations between the component performance functions and provides a joint Gaussian prediction of these functions. This joint Gaussian surrogate model allows the use of MPI to achieve a dynamic trade-off between exploring the regions in the input space with high prediction uncertainty and exploring those that are close to the system limit-state function. The results of three abstract and two practical case studies show that MSRA is capable of achieving better accuracy in estimating the system reliability than the existing surrogate-based methods.

Suggested Citation

  • Sadoughi, Mohammadkazem & Li, Meng & Hu, Chao, 2018. "Multivariate system reliability analysis considering highly nonlinear and dependent safety events," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 189-200.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:189-200
    DOI: 10.1016/j.ress.2018.07.015
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    1. Kleijnen, Jack P.C. & Mehdad, E., 2014. "Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)," Other publications TiSEM 8a096696-f700-4cbe-9474-c, Tilburg University, School of Economics and Management.
    2. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    3. Kim, Youngsuk & Kang, Won-Hee, 2013. "Network reliability analysis of complex systems using a non-simulation-based method," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 80-88.
    4. Yang, Xufeng & Liu, Yongshou & Mi, Caiying & Tang, Chenghu, 2018. "System reliability analysis through active learning Kriging model with truncated candidate region," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 235-241.
    5. Kleijnen, Jack P.C. & Mehdad, Ehsan, 2014. "Multivariate versus univariate Kriging metamodels for multi-response simulation models," European Journal of Operational Research, Elsevier, vol. 236(2), pages 573-582.
    6. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    7. Fauriat, W. & Gayton, N., 2014. "AK-SYS: An adaptation of the AK-MCS method for system reliability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 137-144.
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    Cited by:

    1. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    3. Huang, Shi-Ya & Zhang, Shao-He & Liu, Lei-Lei, 2022. "A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Kleijnen, Jack & van Nieuwenhuyse, I. & van Beers, W.C.M., 2022. "Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions," Other publications TiSEM 903e51c8-bed3-4e97-990f-c, Tilburg University, School of Economics and Management.
    5. Kleijnen, Jack & van Nieuwenhuyse, I. & van Beers, W.C.M., 2022. "Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (revision of 2021-031)," Discussion Paper 2022-015, Tilburg University, Center for Economic Research.
    6. Sheibani, Mohamadreza & Ou, Ge, 2021. "Adaptive local kernels formulation of mutual information with application to active post-seismic building damage inference," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Rabbi, Khan Md. & Sheikholeslami, M. & Karim, Anwarul & Shafee, Ahmad & Li, Zhixiong & Tlili, Iskander, 2020. "Prediction of MHD flow and entropy generation by Artificial Neural Network in square cavity with heater-sink for nanomaterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Liu, Yushan & Li, Luyi & Zhao, Sihan & Song, Shufang, 2021. "A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    11. Moustapha, Maliki & Parisi, Pietro & Marelli, Stefano & Sudret, Bruno, 2024. "Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    12. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    13. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    14. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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