Method for evaluating storage life of regular sampling detection type products

Abstract

The invention discloses a method for evaluating the storage life of regular sampling detection type products. The method comprises the following steps of 1, correcting an upside down phenomenon in original test data by using a PAVA (Pool-Adjacent-Violators Algorithm); 2, obtaining minimum chi-square estimation of parameters of a distribution function, and calculating pearson chi-square statistics; 3, carrying out a pearson chi-square goodness-of-fit test; 4, calculating the reliable service life of the products under a preset storage condition. The method disclosed by the invention has the advantages that the accuracy and the integrality of distribution parameter estimation of the service life are ensured, the requirement of the algorithm on initial values of parameters is lower, the algorithm iteration is quick and simple, and the operability is strong; compared with MLE (Maximum Likelihood Estimation), the requirement on the sample capacity is lower, the evaluation result is more stable, and the superiority during processing small sample data of the method is expressed.

Description

technical field [0001] The invention relates to a method for assessing the storage life of a regular sampling product based on the order-preserving regression theory and the Pearson chi-square goodness-of-fit theory. According to the storage life test data of regular sampling products, it adjusts the distribution parameters of the overall cumulative failure function of the product through the Adjacent Reverse Sequence Merging Algorithm (The Pool-Adjacent-Violators Algorithm, referred to as PAVA algorithm) in the order-preserving regression theory. Minimized chi-square estimation, and the use of goodness-of-fit theory to test the rationality of the distribution function assumption, and finally evaluate the reliable storage life of the product. It is suitable for the evaluation of small samples in the storage life test of regular sampling products and other fields. Background technique [0002] Usually, military products need to be stored in the warehouse for a period of time...

AI Technical Summary

Problems solved by technology

[0007] In actual work, subject to time and cost constraints, the test sample size n i The value cannot be too large, so the test results will be greatly affected by the randomness of sampling, and sometimes X will appear i+1 / n i+1 i / n i This is inconsistent with the characteristics that the failure probability of the sample decreases with the increase of time. In engineering, it is called data inversion. Serious inversion will cause great deviation in the calculation results.

On the other hand, for the incomplete data obtained from the storage life test of periodic sampling products, when applying traditional evaluation methods such as maximum likelihood estimation, without any adjustment and censoring, the result is the cumulative failure function distribution parameter of the product and a conservative estimate of the reliable lifetime

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