Efficient algorithm for analyzing moment-independent global reliability sensitivity

被引：0
|
作者：
Jiang X. [1 ]
Wang Y. [1 ]
Meng M. [1 ]
机构：
[1] Institute of Aircraft, Chinese Flight Test Establishment, Xi'an
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2019年 / 40卷 / 03期
关键词：
Edgeworth expansion; First four-order moments; Integration grid; Moment-independent global reliability sensitivity index; Multiplicative dimensional reduction technique; Sensitivity analysis;
D O I：
10.7527/S1000-6893.2018.22414
中图分类号：
学科分类号：
摘要：
The moment-independent global reliability sensitivity analysis can provide useful information for guiding the reliability-based design optimization. This paper proposes an efficient algorithm for moment-independent global reliability sensitivity analysis based on the multiplicative dimensional reduction technique and the Edgeworth expansion. By adopting Edgeworth expansion, the estimation of the moment-independent global reliability sensitivity index is approximately converted into the estimations of the unconditional and the conditional first four-order moments of the model output and is efficiently estimated by employing the multiplicative dimensional reduction technique. Based on this technique, this paper derives the algorithms of the conditional first four-order moments and the outer expectation of the sensitivity index by repeatedly assembling the information in the integration grid obtained from the estimation process of the unconditional first four-order moments of model output. The proposed algorithm improves the efficiency for analyzing the moment-independent global reliability sensitivity. The analyses of an aeroengine turbine disk and an automobile front axle demonstrate the efficiency and accuracy of the proposed method in estimating the moment-independent global reliability sensitivity index. © 2019, Press of Chinese Journal of Aeronautics. All right reserved.
引用
收藏
相关论文
共 24 条
  • [1] Bolado-Lavin R., Castaings W., Tarantola S., Contribution to the sample mean plot for graphical and numerical sensitivity analysis, Reliability Engineering and System Safety, 94, pp. 1041-1049, (2009)
  • [2] Tarantola S., Kopustinskas V., Bolado-Lavin R., Et al., Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model, Reliability Engineering and System Safety, 99, pp. 62-73, (2012)
  • [3] Wei P.F., Lu Z.Z., Ruan W.B., Et al., Regional sensitivity analysis using revised mean and variance ratio functions, Reliability Engineering and System Safety, 121, pp. 121-135, (2014)
  • [4] Feng K.X., Lyu Z.Z., Jiang X., Efficient algorithm for estimating derivative-based global sensitivity index, . Acta Aeronautica et Astronautica Sinica, 39, 3, (2018)
  • [5] Borgonovo E., Plischke E., Sensitivity analysis: A review of recent advances, European Journal of Operational Research, 3, 1, pp. 869-887, (2016)
  • [6] Saltelli A., Marivoet J., Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques, Reliability Engineering and System Safety, 28, 2, pp. 229-253, (1900)
  • [7] Sobol I.M., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation, 55, pp. 271-280, (2011)
  • [8] Saltelli A., Annoni P., Azzini I., Et al., Variance based sensitivity analysis of model output: Design and estimator for the total sensitivity index, Computation Physics Communication, 181, 2, pp. 259-270, (2010)
  • [9] Gong X.R., Lyu Z.Z., Zuo J.W., Two importance methods for variance-based global sensitivity analysis' W-indices, Acta Aeronautica et Astronautica Sinica, 37, 6, pp. 1888-1898, (2016)
  • [10] Borgonovo E., A new uncertainty importance measure, Reliability Engineering and System Safety, 92, 6, pp. 771-784, (2007)
123