Product Operating Performance Prediction Based on Small-sample Data Augmentation Method
Generally,the production scale of high-precision mechanical products is limited,and there is a complex coupling relationship between assembly characteristic parameters and performances,which is difficult to express by explicit mapping relationship.Building a mapping model that can accurately predict operating performance has practical guiding significance for product assembly and adjustment.To this end,a product operating performance parameter correlation analysis and forecasting method for small-sample data is proposed:The contact stiffness of joint surface is considered to merged into assembly data,and the random forest is adopted to select key characteristics by calculating the importance of assembly feature parameters.The variational autoencoder learning is used to learn prior distribution knowledge of small sample data,then augmented assembly data is generated,and outlier correction is performed on augmented data.The assembly augmented sample is fused with the original assembly data,and the operating performance is predicted by integrated kernel extreme learning machine.Finally,an example of operating performance prediction of a position marker is taken to verify the effectiveness of the proposed method.
small sampledata augmentationcontact characteristicoperating performanceextreme learning machine
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