Statistical Analysis Of Product Reliability Based On Multi-level Data Fusion

The improvement and maintenance of new products need to be supported by effective and accurate reliability assessment.However,the reliability assessment of many products today is limited by the insufficient sample size of the field test,resulting in inaccurate and unreliable assessment.Therefore,this paper constructs two data fusion models,aiming at small sample field data sources,semi-physical simulation data and other test data sources and accelerated life test data sources and other multi-type product life data sources reliability assessment.The main research content of the thesis includes:First,for the life data information of multiple types of test sources of the same product,the three-source data fusion is realized by using the mixed basis distribution model constructed by the common distribution family for product reliability assessment as the basic distribution.On the basis of the EM algorithm,the maximum likelihood estimation of each unknown parameter in the parameter vector of the mixed basis distribution density function is obtained.Then according to the core principle of Bootstrap method,the small sample data source is expanded,and the estimated value of each parameter is given by using this sample,and the density function analysis of mixed basis distribution is determined,and then the product reliability analysis is completed.Finally,it can be seen from the calculation example that the fitting result of the mixed basis distribution model to the data is effective,and the product reliability can be evaluated by this.Secondly,considering that the accelerated life test data is a special life data generated under high stress conditions,if the mixed basis distribution model is used to directly process it,the estimated deviation of the obtained product reliability-related indicators is immeasurable.Through the quantile theory,a method for finding data pairings between different data sources is conceived,and a data mapping model for establishing the correspondence between data sources is constructed.Using the mapping relationship between various data sources,the multi-source data is mapped to the field data source to form a mixed data source,which is used as the basis for the Bayesian statistical analysis of product reliability.Finally,in the case that the product life distribution is in the exponential distribution family and the normal distribution family,for the accelerated life data under different stresses,this data is converted to the distribution density function formula used to determine the parameters under the constant stress level,which is used as Prior Conditions for Bayesian Statistical Analysis of Product Reliability.The Bayesian statistical model and the data mapping model are combined to realize the fusion of multi-source data and estimate the parameter values,obtain the analytical formula of the product density function and complete the product reliability analysis.The simulation example confirms the reliability and effectiveness of this kind of model in application,and is supplemented by the comparison of the fusion effect of two different data mapping models.