A method for statistical inference and confirmation of product performance change and life high confidence small sample

Abstract

A method for statistical inference and confirmation of product performance change and life high confidence small samples includes such steps as (1) establishing a statistical distribution model of product performance change; (2) carrying out parameter estimation and uncertainty quantification of the product performance change distribution model; (3) establishing a product storage life distribution model; (4) carrying out statistical inference of quantitative changes of product performance and storage life. The invention can reduce the required number of sample groups to one group or two groups under various given confidence levels, and the required sample size data of each group can be reduced to five, so that the confidence level of the prescribed reliability evaluation result is not significantly lower than the prescribed confidence level.

Description

technical field [0001] The invention relates to a method for estimating and confirming product performance change and service life, in particular to a statistical inference and confirmation method for product performance change and service life with high confidence and small samples. Background technique [0002] In the design, development, test and batch production of products with high reliability requirements, how to obtain the performance data required by the product through a small number of reliability tests, and quantitatively determine its performance changes through analysis, evaluation and prediction, and Quantitatively determine whether the product meets the specified storage period (life) requirements before and after design improvement and after the product is finalized under the specified storage conditions. However, so far, there is no such convincing, mature, reliable and effective quantitative analysis, evaluation and forecasting methods at home and abroad. ...

AI Technical Summary

Problems solved by technology

These methods need to obtain the laws and models of product performance changes, and require a large amount of product performance comparison data as support, and consume a lot of manpower, material resources, financial resources and time, and the correctness and effectiveness of analysis, evaluation and prediction results Difficulty guaranteeing sex and credibility

In addition, the current method does not deal with the fundamental difference between the performance degradation model and the product performance reliability change model, so there is a large risk of misjudgment using the product performance degradation model and its performance threshold as the criterion for the end of storage life ; while taking the change of product performance reliability in the storage process and the reliability index requirements corresponding to the performance threshold as the storage life end criterion, although it is more in line with the actual use of the product, when the existing method is implemented, the required sample The amount is relatively large, and it is almost impossible to achieve in engineering practice

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