An adaptive extremum response surface method for mechanism action reliability estimation
A high-efficiency calculation method based on adaptive extremum response surface(AERS)is proposed to address the problem of mechanism action reliability estimation under random-interval mixed uncertainty.This is then transformed into the problem of solving the upper and lower bounds of action reliability under random uncer-tainty.The mixed kernel extreme learning machine optimized by the sparrow search algorithm is used to construct the initial response surface from mixed uncertainty variables and the extremum response surface(ERS)from the random variables and transform them into the limit state function(LSF)response value and the LSF response extre-mum,respectively.An adaptive infilling strategy combining active learning and opposition-based learning is then used to select the sample points near the limit state surface to update the ERS and thus improve its accuracy and ef-ficiency.Finally,the approximate solutions of the upper and lower bounds of the action reliability are obtained by the ERS and Monte Carlo simulation.The efficiency and accuracy of the proposed method are then verified by a nu-merical case and an engineering case of a rotary chain conveyor.The proposed method provides a reference for the mechanism action reliability estimation under random-interval mixed uncertainty.
国家科技期刊平台
NSTL
万方数据
维普
