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Article
A System Reliability Method with Dependent Kriging Predictions
Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016, Charlotte, NC)
  • Zhifu Zhu
  • Xiaoping Du, Missouri University of Science and Technology
Abstract
When limit-state functions are highly nonlinear, traditional reliability methods, such as the first order and second order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used, but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Cheaper surrogate models are created for limit-state functions with the minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the dependence between responses from a surrogate model, this method captures the true contribution of each MCS sample to the uncertainty in the estimate of the system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by three examples.
Meeting Name
ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016: Aug. 21-24, Charlotte, NC)
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
  • Computer aided design,
  • Design,
  • Efficiency,
  • Fuel additives,
  • Global optimization,
  • Intelligent systems,
  • Interpolation,
  • Monte Carlo methods,
  • Uncertainty analysis,
  • Kriging methods,
  • Kriging prediction,
  • Limit state functions,
  • Reliability methods,
  • Second-order reliability methods,
  • Surrogate model,
  • System reliability,
  • Training points,
  • Reliability
International Standard Book Number (ISBN)
9780791850114
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
8-1-2016
Citation Information
Zhifu Zhu and Xiaoping Du. "A System Reliability Method with Dependent Kriging Predictions" Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016, Charlotte, NC) Vol. 2B-2016 (2016)
Available at: http://works.bepress.com/xiaoping-du/11/