Reliability analysis and design considering disjointed active failure regions

Failure of practical engineering systems could be induced by several correlated failure modes, and consequently reliability analysis are conducted with multiple disjointed failure regions in the system random input space. Problems with disjointed failure regions create a great challenge for existing reliability analysis approaches due to the discontinuity of the system performance function between these regions. This paper presents a new enhanced Monte Carlo simulation (EMCS) approach for reliability analysis and design considering disjointed failure regions. The ordinary Kriging method is adopted to construct surrogate model for the performance function so that Monte Carlo simulation (MCS) can be used to estimate the reliability. A maximum failure potential based sampling scheme is developed to iteratively search failure samples and update the Kriging model. Two case studies are used to demonstrate the efficacy of the proposed methodology.Copyright © 2015 by ASME

[1]  Samy Missoum.  Controlling structural failure modes during an impact in the presence of uncertainties , 2007 .

[2]  Zhenshan Cui,et al.  Uncertainty quantification using polynomial chaos expansion with points of monomial cubature rules , 2008 .

[3]  Antanas Zilinskas,et al.  A review of statistical models for global optimization , 1992, J. Glob. Optim..

[4]  Jiten Patel,et al.  Classification approach for reliability-based topology optimization using probabilistic neural networks , 2012 .

[5]  Zissimos P. Mourelatos,et al.  On Estimating the Reliability of Multiple Failure Region Problems Using Approximate Metamodels , 2009 .

[6]  B. Youn,et al.  Bayesian reliability-based design optimization using eigenvector dimension reduction (EDR) method , 2008 .

[7]  R. Rackwitz,et al.  Structural reliability under combined random load sequences , 1978 .

[8]  B. Youn,et al.  Eigenvector dimension reduction (EDR) method for sensitivity-free probability analysis , 2008 .

[9]  S. Rahman,et al.  Decomposition methods for structural reliability analysis , 2005 .

[10]  Olivier P. Le Maître,et al.  Polynomial chaos expansion for sensitivity analysis , 2009, Reliab. Eng. Syst. Saf..

[11]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[12]  K. Choi,et al.  System reliability-based design optimization using the MPP-based dimension reduction method , 2010 .

[13]  Antonio Harrison Sánchez,et al.  Limit state function identification using Support Vector Machines for discontinuous responses and disjoint failure domains , 2008 .

[14]  Byeng D. Youn,et al.  Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems , 2011 .

[15]  Ramana V. Grandhi,et al.  Improved Distributed Hypercube Sampling , 2002 .