System reliability analysis through active learning Kriging model with truncated candidate region

System reliability analysis (SRA) with multiple failure modes is researched in this paper. Active learning Kriging (ALK) model which only finely approximates the performance function in the narrow region close to the limit state has shown great potential and several strategies based on ALK model have been proposed. The key of SRA based on ALK model is to identify the components with little contribution to system failure and avoid approximating them. However, we figure out that the existing strategies fail to fulfill this task if large numerical difference exists among the values of component performance functions. Therefore, a brand-new theory on identifying the unimportant component(s) is proposed. Based on this theory, the method based on ALK model with a truncated candidate region (TCR) is proposed and it is termed as ALK-TCR. ALK-TCR is capable to recognize and avoid approximating the unimportant component(s), even if large numerical difference arises among the components. Its high performance is demonstrated by three complicated examples.

[1]  B. Youn,et al.  Complementary Intersection Method for System Reliability Analysis , 2009 .

[2]  B. Sudret,et al.  Reliability-based design optimization using kriging surrogates and subset simulation , 2011, 1104.3667.

[3]  Nicolas Gayton,et al.  AK-SYS: An adaptation of the AK-MCS method for system reliability , 2014, Reliab. Eng. Syst. Saf..

[4]  Jian Wang,et al.  LIF: A new Kriging based learning function and its application to structural reliability analysis , 2017, Reliab. Eng. Syst. Saf..

[5]  Chao Hu,et al.  A Generalized Complementary Intersection Method (GCIM) for System Reliability Analysis , 2011 .

[6]  Yong Yao,et al.  Dynamic tunneling algorithm for global optimization , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Ling Li,et al.  Sequential design of computer experiments for the estimation of a probability of failure , 2010, Statistics and Computing.

[8]  R. Rackwitz,et al.  First-order concepts in system reliability , 1982 .

[9]  J. Ching,et al.  Evaluating small failure probabilities of multiple limit states by parallel subset simulation , 2010 .

[10]  Sankaran Mahadevan,et al.  Adaptive simulation for system reliability analysis of large structures , 2000 .

[11]  Hong-shuang Li,et al.  A generalized Subset Simulation approach for estimating small failure probabilities of multiple stochastic responses , 2015 .

[12]  Xiaoping Du.  System reliability analysis with saddlepoint approximation , 2010 .

[13]  Zhen Hu,et al.  Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis , 2016 .

[14]  Erik Kjeang,et al.  Modification of DIRECT for high-dimensional design problems , 2014 .

[15]  Maurice Lemaire,et al.  Assessing small failure probabilities by combined subset simulation and Support Vector Machines , 2011 .

[16]  M. Eldred,et al.  Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions , 2008 .

[17]  E. Castillo,et al.  Uncertainty analyses in fault trees and Bayesian networks using FORM/SORM methods , 1999 .

[18]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[19]  Paolo Gardoni,et al.  Matrix-based system reliability method and applications to bridge networks , 2008, Reliab. Eng. Syst. Saf..

[20]  O. Ditlevsen.  Narrow Reliability Bounds for Structural Systems , 1979 .

[21]  Xiaoping Du,et al.  A System Reliability Method with Dependent Kriging Predictions , 2016, DAC 2016.

[22]  Yi Gao,et al.  An active learning kriging model for hybrid reliability analysis with both random and interval variables , 2015 .

[23]  George Michailidis,et al.  Sequential Experiment Design for Contour Estimation From Complex Computer Codes , 2008, Technometrics.

[24]  Carlos Guedes Soares,et al.  Adaptive surrogate model with active refinement combining Kriging and a trust region method , 2017, Reliab. Eng. Syst. Saf..

[25]  Nicolas Gayton,et al.  AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .

[26]  Pingfeng Wang,et al.  An Integrated Performance Measure Approach for System Reliability Analysis , 2015 .

[27]  Guillaume Perrin,et al.  Active learning surrogate models for the conception of systems with multiple failure modes , 2016, Reliab. Eng. Syst. Saf..

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

[29]  Manolis Papadrakakis,et al.  Accelerated subset simulation with neural networks for reliability analysis , 2012 .

[30]  Sankaran Mahadevan,et al.  Resilience Assessment Based on Time-Dependent System Reliability Analysis , 2016 .

[31]  Sankaran Mahadevan,et al.  Efficient surrogate models for reliability analysis of systems with multiple failure modes , 2011, Reliab. Eng. Syst. Saf..