EWMA图表
控制图
泊松分布
统计
统计过程控制
数学
零膨胀模型
泊松回归
计数数据
理论(学习稳定性)
负二项分布
复合泊松分布
复合泊松过程
计算机科学
过程(计算)
泊松过程
人口
操作系统
机器学习
社会学
人口学
作者
Anwaar M. Hassan,Anwar A. Aly
标识
DOI:10.1080/03610918.2022.2050393
摘要
In many quality control applications, the quality of some products or processes is best characterized by a functional relationship (profile) between a response variable and one or more explanatory variables. Profile monitoring involves the use of control charts to monitor the stability of this type of quality control processes. Several studies have discussed the problem of monitoring normal response profiles. More recently, researchers started studying the case where the response variable follows a discrete distribution such as the Poisson or the Bernoulli distributions. Due to recent technological advancement and the high level of automation used in almost all manufacturing processes, there exist near-zero defect manufacturing processes, hence, the zero-inflated Poisson distribution is expected to be more appropriate than the ordinary Poisson distribution for monitoring such processes. This study aims at extending three of the existing methods for phase II monitoring of profiles namely; MEWMA, Hotelling’s T2, and EWMA-R to the case of zero-inflated Poisson profiles. A simulation study is used to compare the performance of the competing approaches in terms of the average run length (ARL) and the standard deviation run length (SDRL). The results revealed that the EWMA-R chart is generally superior to the other competing methods.
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