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Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model

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  • Cui, Da
  • Wang, Guoqiang
  • Lu, Yanpeng
  • Sun, Kangkang

Abstract

Based on stress-strength interference theory, calculating the reliability by using the coefficient of variation is a common method for the optimal design of a planetary transmission system. This method calculates the reliability by assuming the tangential force is zero while this is quite different from the real working condition. In this paper, the authors propose a novel reliability design and optimization method of the planetary gear, using the genetic algorithm, based on Kriging model. The Kriging method is used to establish the gear reliability model to simplify the reliability calculation. Then the Kriging model is optimized by using the genetic algorithm to ensure the global optimal solution. To simulate the real working conditions of planetary gears, the discrete element method (DEM) is adopted to calculates the load variation coefficient of the planetary gear. By taking the double toothed roller crusher as case study, the optimization results show that proposed method can significantly improve the calculation efficiency, and compared with the traditional design, the volume increases by 36.96%, and the failure rate of the planetary gear decreases by 17.05%.

Suggested Citation

  • Cui, Da & Wang, Guoqiang & Lu, Yanpeng & Sun, Kangkang, 2020. "Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020305755
    DOI: 10.1016/j.ress.2020.107074
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    References listed on IDEAS

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    1. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    2. Shi, Yan & Lu, Zhenzhou & He, Ruyang & Zhou, Yicheng & Chen, Siyu, 2020. "A novel learning function based on Kriging for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Sonja Kuhnt & David Steinberg, 2010. "Design and analysis of computer experiments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(4), pages 307-309, December.
    4. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    5. Wang, Zeyu & Shafieezadeh, Abdollah, 2019. "REAK: Reliability analysis through Error rate-based Adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 33-45.
    6. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    7. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
    8. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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    Cited by:

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