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An innovative Bayesian sequential censored sampling inspection method and application to test design

机译：一种创新的贝叶斯顺序检查抽样检验方法及其在测试设计中的应用

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This paper proposes an innovative Bayesian sequential censored sampling inspection method to improve the inspection level and reduce the sample size in acceptance test plans for continuous lots. A mathematical model of Bayesian sequential censored sampling is built, where a new inspection parameter is created and two types of risk are modified. As the core of Bayesian risk formulas, a new structure method of the prior distribution is presented by combining the empirical distribution with the uncertainty of the estimation. To improve the fitting accuracy of parameter estimation, an adaptive genetic algorithm is applied and compared with different parameter estimation methods. In the prior distribution, a prior estimator is introduced to design a sampling plan for continuous lots. Then, three types of producer's and consumer's risks are derived and compared. The simulation results indicate that the modified Bayesian sampling method performs well, with the lowest risks and the smallest sample size. Finally, a new sequential censored sampling plan for continuous lots is designed for the accuracy acceptance test of an aircraft. The test results show that compared with the traditional single sampling plan, the sample size is reduced by 66.7%, saving a vast amount of test costs. (C) 2019 Elsevier Inc. All rights reserved.
机译：本文提出了一种创新的贝叶斯顺序检查抽样检验方法，以提高检验水平，并减少连续批次验收测试计划中的样本量。建立贝叶斯顺序删失抽样的数学模型，在其中创建新的检查参数并修改两种风险。作为贝叶斯风险公式的核心，结合经验分布和估计的不确定性，提出了一种先验分布的新结构方法。为了提高参数估计的拟合精度，应用了自适应遗传算法，并将其与不同的参数估计方法进行了比较。在先验分配中，引入了先验估计器来设计连续批次的抽样计划。然后，推导并比较了三种生产者和消费者的风险。仿真结果表明，改进的贝叶斯采样方法性能良好，风险最小，样本量最小。最后，针对飞机的精度验收测试，设计了一个新的连续批检查顺序抽样计划。测试结果表明，与传统的单次抽样方案相比，样本量减少了66.7％，节省了大量的测试成本。 （C）2019 Elsevier Inc.保留所有权利。

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