A Method Of Small Sample Reliability Assessment Based On Bayesian Theory

With the rapid development of science and technology,high-tech products due to high cost,complex production process and other reasons,can not be a large sample of the reliability test,which makes the test data show a small sample of the state.How to scientifically and rational use the small samples and provide decision-making basis for the product has become an important issue.In recent years,the Bayesian theory can effectively integrate the prior test information with a small amount of field test information to make an objective evaluation of the reliability of the system,which has received extensive attention.Therefore,this paper studies the small sample reliability evaluation method based on Bayesian theory.The main contents are listed as follows.In this paper,for the problem of how to improve the accuracy of Bayesian small sample reliability evaluation,we improve the pre-test information preprocessing and Bayesian reliability evaluation.In the preprocessing stage of pre-test information,the conversion of pre-test information is mainly studied.Aiming at the problem of information conversion in similar systems,this paper presents a novel method,which based on the correlation coefficient to determine the relationship between similar systems,and the use of D-S evidence theory to integrate the conversion method;At the same time,In this paper,an improved method based on improved HS algorithm is proposed to improve the accuracy of information partitioning and matching,and then improve the conversion efficiency.In the Bayesian reliability evaluation stage,the distribution of pre-test information is determined by using the method of conjugate pre-distribution distribution,and a mixed pre-test model is proposed to solve the problem of pre-test information "submerged" small sample field test information and the problem of multi-source pre-test distribution of the weight;The Bayesian formula is used to integrate the pre-test distribution model and the field test information effectively.The Gibbs algorithm is used to sample the post-distribution model,and the evaluation results of the reliability parameters are obtained effectively.The experimental results show that the pre-test information conversion method proposed in this paper can improve the accuracy of translation in the premise that the pre-test information and the field test information meet the compatibility test.The Bayesian reliability evaluation method is used to solve the problem of fusion weight of multi-source pre-test distribution,and the reliability parameters are evaluated effectively.The feasibility of the method is demonstrated by three experiments.