A system active learning Kriging method for system reliability-based design optimization with a multiple response model

•A new method is proposed for system reliability-based design optimization.•The responses of all constraints from a multiple response model are considered.•Three system active learning functions are developed for different systems.•Results of four examples verify the accuracy and efficiency of propo...

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Veröffentlicht in:Reliability engineering & system safety Jg. 199; S. 106935 - 11
Hauptverfasser: Xiao, Mi, Zhang, Jinhao, Gao, Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Barking Elsevier Ltd 01.07.2020
Elsevier BV
Schlagworte:
Active learning
Computer applications
Confidence intervals
Design
Design optimization
Harvesters
Kriging
Learning
Limit states
Monte Carlo simulation
Multiple response model
Random variables
Reliability engineering
Sensitivity analysis
Statistical analysis
System active learning function
System failure probability
System reliability
System reliability-based design optimization (SRBDO)
Upgrading
Multiple response model
System reliability-based design optimization (SRBDO)
System active learning function
Kriging
System failure probability
ISSN:0951-8320, 1879-0836
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Zusammenfassung:•A new method is proposed for system reliability-based design optimization.•The responses of all constraints from a multiple response model are considered.•Three system active learning functions are developed for different systems.•Results of four examples verify the accuracy and efficiency of proposed method. This paper proposes a system active learning Kriging (SALK) method to handle system reliability-based design optimization (SRBDO) problems, where responses of all constraints at an input can be obtained simultaneously by running a multiple response model. In SALK, to select update points around the limit-state surfaces, three new system active learning functions are respectively defined for parallel, series and combined systems. The confidence interval of estimation of system failure probability at intermediate SRBDO solutions is considered in the stopping condition of Kriging update to reduce unnecessary update points used for refining the region far from the final SRBDO solution. Based on updated Kriging models, system failure probability is estimated by Monte Carlo simulation (MCS), and its partial derivative with respect to random variables is calculated by stochastic sensitivity analysis. The efficiency of the proposed SALK method for SRBDO is validated by four examples, including a power harvester design. The results indicate that SALK can locally approximate the limit-state surfaces around the final SRBDO solution and efficiently reduce the computational cost on the refinement of the region far from the final SRBDO solution.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2020.106935