In this industry 4.0 revolution, most of the manufacturing processes are equipped with the digital devices which are continuously recording the data. To monitor the quality of a manufacturing system, variable about number of conforming or nonconforming items is usually used and statistical analysis based on it is further utilized for developing the policies. In this era of sophisticated and modern technology, most of the manufacturing systems are producing near zero-defect items. Such processes are known as high-quality processes, and their dataset consists of excess number of zeros. Generally, the zero excess or near zero-defect dataset is well fitted by the zero-inflated distributions, and the zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) distributions are the most common models. Most of the time, in high-quality processes, few covariates are also measured along with defect counts. Hence, to model such processes, generalized linear model (GLM) based on ZIP and ZINB distributions are used to fit the data. In monitoring perspective, data-based control charts are designed to monitor high-quality datasets while the GLM-based control charts based on the residuals of the GLM models are used to monitor a change in the mean of the zero excess datasets. In this study, we have developed memory-type data-based and GLM-based control charts (i.e., exponentially weighted moving average and cumulative sum) to monitor the increasing average defect counts in a high-quality process. Further, the proposed methods are evaluated using run-length properties and compared with its competitive charts. Furthermore, to highlight the importance of the study, the proposed methods are implemented on a dataset concerning the number of flight delays between Atlanta and Orlando airports.

中文翻译：

---

基于广义线性模型的指数加权移动平均和累积总和图用于高质量过程的监控

在这场工业 4.0 革命中，大多数制造过程都配备了连续记录数据的数字设备。为了监控制造系统的质量，通常使用关于合格或不合格项目数量的变量，并进一步利用基于它的统计分析来制定政策。在这个尖端和现代技术的时代，大多数制造系统都在生产接近零缺陷的产品。此类流程称为高质量流程，其数据集包含过多的零。通常，零过剩或接近零缺陷的数据集可以很好地拟合零膨胀分布，而零膨胀泊松 (ZIP) 和零膨胀负二项式 (ZINB) 分布是最常见的模型。大多数时候，在高质量过程中，很少有协变量与缺陷计数一起测量。因此，为了对此类过程建模，使用基于 ZIP 和 ZINB 分布的广义线性模型 (GLM) 来拟合数据。在监控方面，基于数据的控制图用于监控高质量数据集，而基于 GLM 模型残差的基于 GLM 的控制图用于监控零超额数据集的均值变化。在这项研究中，我们开发了基于内存型数据和基于 GLM 的控制图（即指数加权移动平均和累积总和），以监控高质量过程中不断增加的平均缺陷计数。此外，所提出的方法使用游程特性进行评估，并与其竞争图表进行比较。此外，为了强调这项研究的重要性，