Time-variant reliability analysis using the parallel subset simulation

Time-variant reliability problems commonly occur in practical engineering applications due to deterioration in material properties, dynamic load and other causes. Since this kind of problem is usually a small probability event, subset simulation is more efficient than Monte Carlo simulation (MCS). However, subset simulation can only focus on a single limit function when propagating the conditional samples. Parallel subset simulation is applied to deal with time-dependent reliability analysis in this paper. A new method is proposed to construct a function called “principal variable†. The “principal variable†can represent limit state at each time instant to generate conditional samples. In addition, the update procedure of “principal variable†should be set at each simulation stage to keep the correlations between “principal variable†and nt limit states strong. Two numerical examples are used to demonstrate the effectiveness and accuracy of the developed parallel subset simulation for time-variant reliability analysis.

[1]  Zhen Hu,et al.  Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis , 2015 .

[2]  Zissimos P. Mourelatos,et al.  Design for Lifecycle Cost Using Time-Dependent Reliability Analysis , 2008 .

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

[4]  Ming Jian Zuo,et al.  A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis , 2018, Reliab. Eng. Syst. Saf..

[5]  Zequn Wang,et al.  Time-variant reliability assessment through equivalent stochastic process transformation , 2016, Reliab. Eng. Syst. Saf..

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

[7]  Qiujing Pan,et al.  Sliced inverse regression-based sparse polynomial chaos expansions for reliability analysis in high dimensions , 2017, Reliab. Eng. Syst. Saf..

[8]  Pingfeng Wang,et al.  A Nested Extreme Response Surface Approach for Time-Dependent Reliability-Based Design Optimization , 2012 .

[9]  Weiwen Peng,et al.  Life cycle reliability assessment of new products - A Bayesian model updating approach , 2013, Reliab. Eng. Syst. Saf..

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

[11]  James L. Beck,et al.  Reliability Estimation for Dynamical Systems Subject to Stochastic Excitation using Subset Simulation with Splitting , 2005 .

[12]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

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

[14]  Ahsan Kareem,et al.  Analysis and simulation tools for wind engineering , 1997 .

[15]  Z. Mourelatos,et al.  Time-Dependent Reliability of Dynamic Systems Using Subset Simulation With Splitting Over a Series of Correlated Time Intervals , 2013, DAC 2013.

[16]  Enrico Zio,et al.  Simulation-based exploration of high-dimensional system models for identifying unexpected events , 2017, Reliab. Eng. Syst. Saf..

[17]  X. Shao,et al.  A local Kriging approximation method using MPP for reliability-based design optimization , 2016 .

[18]  Zissimos P. Mourelatos,et al.  Time-Dependent Reliability Analysis Using the Total Probability Theorem , 2014, DAC 2014.

[19]  S. Winterstein Nonlinear Vibration Models for Extremes and Fatigue , 1988 .

[20]  Zissimos P. Mourelatos,et al.  An Importance Sampling Approach for Time-Dependent Reliability , 2011, DAC 2011.

[21]  Siu-Kui Au,et al.  Engineering Risk Assessment with Subset Simulation , 2014 .

[22]  James L. Beck,et al.  Hybrid Subset Simulation Method for Dynamic Reliability Problems , 2005 .

[23]  Zeyu Wang,et al.  REAK: Reliability analysis through Error rate-based Adaptive Kriging , 2019, Reliab. Eng. Syst. Saf..

[24]  Hongping Zhu,et al.  Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation , 2016 .

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

[26]  Zhenzhou Lu,et al.  An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy , 2019, Reliab. Eng. Syst. Saf..

[27]  Sankaran Mahadevan,et al.  Bayesian model updating with summarized statistical and reliability data , 2018, Reliab. Eng. Syst. Saf..

[28]  Bruno Sudret,et al.  Analytical derivation of the outcrossing rate in time-variant reliability problems , 2008 .

[29]  Yan Dong,et al.  Time-variant fatigue reliability assessment of welded joints based on the PHI2 and response surface methods , 2018, Reliab. Eng. Syst. Saf..

[30]  Siu-Kui Au,et al.  Application of subset simulation methods to reliability benchmark problems , 2007 .

[31]  Dequan Zhang,et al.  A time-variant reliability analysis method based on stochastic process discretization , 2014 .

[32]  Kok-Kwang Phoon,et al.  Convergence study of the truncated Karhunen–Loeve expansion for simulation of stochastic processes , 2001 .

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

[34]  M. Grigoriu Crossings of non-gaussian translation processes , 1984 .

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

[36]  Wei Chen,et al.  Confidence-based adaptive extreme response surface for time-variant reliability analysis under random excitation , 2017 .

[37]  Kurtis R. Gurley,et al.  Efficient stationary multivariate non-Gaussian simulation based on a Hermite PDF model , 2015 .

[38]  Pramudita Satria Palar,et al.  Global sensitivity analysis via multi-fidelity polynomial chaos expansion , 2017, Reliab. Eng. Syst. Saf..

[39]  Xiaoping Du,et al.  Time-dependent reliability analysis with joint upcrossing rates , 2013 .

[40]  Wang Jian,et al.  Two accuracy measures of the Kriging model for structural reliability analysis , 2017 .

[41]  Pingfeng Wang,et al.  A new approach for reliability analysis with time-variant performance characteristics , 2013, Reliab. Eng. Syst. Saf..

[42]  C. S. Manohar,et al.  Experimental estimation of time variant system reliability of vibrating structures based on subset simulation with Markov chain splitting , 2018, Reliab. Eng. Syst. Saf..

[43]  S. Rice Mathematical analysis of random noise , 1944 .

[44]  Pingfeng Wang,et al.  A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis , 2015, Reliab. Eng. Syst. Saf..

[45]  Bruno Sudret,et al.  The PHI2 method: a way to compute time-variant reliability , 2004, Reliab. Eng. Syst. Saf..

[46]  Sankaran Mahadevan,et al.  A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis , 2016 .