Reliability Analysis Based on Active Learning Kriging Model
ZHAI Weihao， GONG Minhao， LIN Mingrun， KUANG Tingyu， WEN Shanshan
Shanghai Aerospace Equipment Manufacturer， Shanghai 200245， China
Abstract In practical aerospace engineering, due to the high reliability and low failure probability, using the traditional Monte Carlo method to obtain accurate solution needs huge computation. The paper combines Latin Hypercube Sampling and Kriging surrogate model to get the minimum fitting error model by using the EFF learning mechanism for active learning INITIAL Kriging model, then uses Monte Carlo Method for reliability calculation, which greatly reduces the computation. At last, the method is verified by three numerical examples.
Key words ：
reliability
Latin Hypercube Sampling
Kriging surrogate model
EFF learning mechanism
Received: 08 June 2022
Published: 30 March 2023
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