EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces

2020 ◽  
Vol 198 ◽  
pp. 106906 ◽  
Author(s):  
Chen Jiang ◽  
Haobo Qiu ◽  
Liang Gao ◽  
Dapeng Wang ◽  
Zan Yang ◽  
...  
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Estimation Error ◽  
Limit State ◽  
System Reliability Analysis ◽  
Kriging Approximation ◽  
Adaptive Kriging

scholarly journals Probabilistic Approach to System Reliability of Mechanism with Correlated Failure Models

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Xianzhen Huang ◽  
Yimin Zhang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Failure Modes ◽  
Limit State ◽  
Probabilistic Approach ◽  
Upper And Lower Bounds ◽  
State Function ◽  
System Reliability Analysis ◽  
Kinematic Performance ◽  
Planar Linkages

In this paper, based on the kinematic accuracy theory and matrix-based system reliability analysis method, a practical method for system reliability analysis of the kinematic performance of planar linkages with correlated failure modes is proposed. The Taylor series expansion is utilized to derive a general expression of the kinematic performance errors caused by random variables. A proper limit state function (performance function) for reliability analysis of the kinematic performance of planar linkages is established. Through the reliability theory and the linear programming method the upper and lower bounds of the system reliability of planar linkages are provided. In the course of system reliability analysis, the correlation of different failure modes is considered. Finally, the practicality, efficiency, and accuracy of the proposed method are shown by a numerical example.

Efficient Kriging surrogate modeling approach for system reliability analysis

2017 ◽  
Vol 31 (2) ◽  
pp. 143-160 ◽  
Author(s):  
Zhen Hu ◽  
Saideep Nannapaneni ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Surrogate Model ◽  
Limit State ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Stopping Criterion ◽  
Limit States ◽  
System Reliability Analysis ◽  
Kriging Surrogate Modeling

AbstractCurrent limit state surrogate modeling methods for system reliability analysis usually build surrogate models for failure modes individually or build composite limit states. In practical engineering applications, multiple system responses may be obtained from a single setting of inputs. In such cases, building surrogate models individually will ignore the correlation between different system responses and building composite limit states may be computationally expensive because the nonlinearity of composite limit state is usually higher than individual limit states. This paper proposes a new efficient Kriging surrogate modeling approach for system reliability analysis by constructing composite Kriging surrogates through selection of Kriging surrogates constructed individually and Kriging surrogates built based on singular value decomposition. The resulting composite surrogate model will combine the advantages of both types of Kriging surrogate models and thus reduce the number of required training points. A new stopping criterion and a new surrogate model refinement strategy are proposed to further improve the efficiency of this approach. The surrogate models are refined adaptively with high accuracy near the active failure boundary until the proposed new stopping criterion is satisfied. Three numerical examples including a series, a parallel, and a combined system are used to demonstrate the effectiveness of the proposed method.

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