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Optimum reliable design of rolling element bearings using multi-objective optimization based on C-NSGA-II

Author

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  • Baklouti, Ahmad
  • Dammak, Khalil
  • El Hami, Abdelkhalak

Abstract

The dynamic behavior of a mechanical system is affected by the properties of the bearing under actual operating conditions. For that, rolling bearing design must provide a variety of constraints, including geometrical, kinematic and strength requirements, while providing high performance, long service life and reliability. In this work, a multi-objective optimization based on reliability analysis (MORBDO) is carried out to determine the most efficient rolling bearing internal geometrical parameters. The contribution of this work lies in the integration of the reliability of the bearing in the optimization algorithm by including the uncertainty of the internal geometric parameters of the bearing. The obtained results proved that the multi-objective based reliability optimization problem combining the hybrid method (HM) and the constrained non-dominated sorting genetic algorithm (C-NSGA-II) was able to generate reliable Pareto solutions that are well distributed with respect to the required reliability.

Suggested Citation

  • Baklouti, Ahmad & Dammak, Khalil & El Hami, Abdelkhalak, 2022. "Optimum reliable design of rolling element bearings using multi-objective optimization based on C-NSGA-II," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001673
    DOI: 10.1016/j.ress.2022.108508
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    References listed on IDEAS

    as
    1. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Zhang, Xiaobo & Lu, Zhenzhou & Cheng, Kai, 2021. "Reliability index function approximation based on adaptive double-loop Kriging for reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Eric Bradford & Artur M. Schweidtmann & Alexei Lapkin, 2018. "Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm," Journal of Global Optimization, Springer, vol. 71(2), pages 439-440, June.
    4. Leimeister, Mareike & Kolios, Athanasios, 2021. "Reliability-based design optimization of a spar-type floating offshore wind turbine support structure," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    5. Khalil Dammak & Abdelkhalak El Hami, 2020. "Multi-objective reliability based design optimization using Kriging surrogate model for cementless hip prosthesis," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 23(12), pages 854-867, September.
    6. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Eric Bradford & Artur M. Schweidtmann & Alexei Lapkin, 2018. "Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm," Journal of Global Optimization, Springer, vol. 71(2), pages 407-438, June.
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    Cited by:

    1. Jiang, Zhiyuan & Huang, Xianzhen & Wang, Bingxiang & Liao, Xin & Liu, Huizhen & Ding, Pengfei, 2024. "Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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