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A hybrid Gaussian process model for system reliability analysis

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

Abstract

This paper proposes a new system reliability analysis method based on a hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models. The proposed method is named hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of an engineered system according to their interdependencies: MGP models are constructed over the groups of highly interdependent components and UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to learn and quantify the pairwise dependencies between the components involving both linear and complex nonlinear dependency patterns. An initial hybrid Gaussian process (HGP) model comprising both UGP and MGP models is first constructed with a set of near-random samples and these UGP and MGP models are then updated with new samples that are sequentially identified based on a new acquisition function named adaptive multivariate probability of improvement. Results of three mathematical and two real-world engineering case studies suggest that the proposed HGP-SRA method can achieve better accuracy and efficiency in system reliability estimation than several benchmark surrogate-based methods.

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  • 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).
    • Handle: RePEc:eee:reensy:v:197:y:2020:i:c:s0951832019302972
    DOI: 10.1016/j.ress.2020.106816
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    1. Bishop, Peter & Povyakalo, Andrey, 2017. "Deriving a frequentist conservative confidence bound for probability of failure per demand for systems with different operational and test profiles," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 246-253.
    2. Perrin, G., 2016. "Active learning surrogate models for the conception of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 130-136.
    3. Nannapaneni, Saideep & Mahadevan, Sankaran, 2016. "Reliability analysis under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 9-20.
    4. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    5. 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.
    6. Hawchar, Lara & El Soueidy, Charbel-Pierre & Schoefs, Franck, 2017. "Principal component analysis and polynomial chaos expansion for time-variant reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 406-416.
    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.
    8. Xiao, Ning-Cong & Zuo, Ming J. & Zhou, Chengning, 2018. "A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 330-338.
    9. Stern, R.E. & Song, J. & Work, D.B., 2017. "Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 1-9.
    10. 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.
    11. Rahman, Sharif, 2011. "Global sensitivity analysis by polynomial dimensional decomposition," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 825-837.
    12. 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.
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