An Effective Kriging-based Approach for System Reliability Analysis with Multiple Failure Modes

Kriging-based surrogate model is widely adopted in the area of component reliability analysis, thanks to its computational efficiency. However, surrogate models employed in component reliability analysis can not be employed directly in system reliability analysis. In this paper, an effective krigingbased system reliability analysis approach is proposed based on AK-SYS. In the proposed method, the best next samples are selected from the safe region or failure region, and samples in the areas that have little contribution to the composite performance function are avoided. The efficiency and accuracy of the proposed method are illustrated via a numerical example.

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

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

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

[4]  Joeri Van Mierlo,et al.  Status and future perspectives of reliability assessment for electric vehicles , 2019, Reliab. Eng. Syst. Saf..

[5]  Yongshou Liu,et al.  System reliability analysis through active learning Kriging model with truncated candidate region , 2018, Reliab. Eng. Syst. Saf..

[6]  Zhenzhou Lu,et al.  AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function , 2018, Structural and Multidisciplinary Optimization.

[7]  Dan M. Frangopol,et al.  Time-variant reliability analysis of widened deteriorating prestressed concrete bridges considering shrinkage and creep , 2017 .

[8]  S. Marelli,et al.  An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis , 2017, Structural Safety.

[9]  Ning-Cong Xiao,et al.  Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables , 2020 .

[10]  Shaojun Li,et al.  Adaptive reliability analysis based on a support vector machine and its application to rock engineering , 2017 .

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

[12]  Linhan Ouyang,et al.  A new ensemble modeling approach for reliability-based design optimization of flexure-based bridge-type amplification mechanisms , 2020, The International Journal of Advanced Manufacturing Technology.

[13]  Xiaoxu Huang,et al.  AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis , 2020 .

[14]  Changqing Gong,et al.  Importance sampling-based system reliability analysis of corroding pipelines considering multiple failure modes , 2018, Reliab. Eng. Syst. Saf..

[15]  A Henriques,et al.  An innovative adaptive sparse response surface method for structural reliability analysis , 2018, Structural Safety.

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

[17]  Fausto Pedro García Márquez,et al.  Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines , 2019, Reliab. Eng. Syst. Saf..