论文部分内容阅读
针对参数随机化情况下生产过程能力的评价问题,提出了新的过程能力指数估计与评价方法。通过质量控制模型的统计结构分析,研究了扩散先验分布下参数后验分布,据此构造了过程能力指数的贝叶斯点估计和区间估计;在此基础上,将前一阶段模型参数后验分布作为下一阶段的参数先验分布,充分利用历史数据信息,建立了过程能力指数及其下限的贝叶斯动态评价模型。研究结果表明:与现有的贝叶斯过程能力指数估计方法比较,贝叶斯动态过程能力指数的预测精度优于前者,更能反映实际生产过程能力水平。
Aiming at the problem of evaluating the ability of production process in case of parameter randomization, a new method of estimating and evaluating process capability index is proposed. Through the statistical structure analysis of the quality control model, we study the posterior distribution of parameters under the prior distribution of diffusion, and construct the Bayesian point estimate and interval estimation of process capability index. Based on this, As a parameter distribution of the next stage, the distribution of test distribution takes full advantage of historical data information, and establishes a Bayesian dynamic evaluation model of process capability index and its lower bound. The results show that compared with the existing methods for estimating Bayesian process capability index, the prediction accuracy of Bayesian dynamic process capability index is better than the former, which can better reflect the actual production process capability level.