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Generalized linear model based monitoring methods for high‐yield processes
Quality and Reliability Engineering International ( IF 2.3 ) Pub Date : 2020-03-03 , DOI: 10.1002/qre.2646
Tahir Mahmood 1
Affiliation  

Emerge in technology brought well‐organized manufacturing systems to produce high‐quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero‐defect and modeled based on zero‐inflated distributions. The zero‐inflated Poisson (ZIP) and zero‐inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high‐yield and rare health‐related processes. Therefore, data‐based control charts under ZIP and ZINB distributions (i.e., Y‐ZIP and Y‐ZINB) are proposed for the monitoring of high‐quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR‐ZIP and PR‐ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model‐based charts are compared with the existing data‐based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.

中文翻译:

基于广义线性模型的高产过程监控方法

新兴技术带来了组织良好的制造系统来生产高质量的物品。因此,产品的监视和控制已成为质量检查人员的一项艰巨任务。通过这些高效的过程,生产的物品大多为零缺陷,并基于零膨胀的分布建模。零膨胀泊松(ZIP)和零膨胀负二项式(ZINB)分布是最常见的分布,用于对高收益和罕见的健康相关过程进行建模。因此,建议使用ZIP和ZINB分布下的基于数据的控制图(即,Y-ZIP和Y-ZINB)来监视高质量的过程。通常,利用缺陷计数,在此过程中也很少测量协变量,并且使用基于ZIP和ZINB分布的广义线性模型来估计其参数。在本研究中,我们基于ZIP和ZINB回归模型设计了监视结构(即PR-ZIP和PR-ZINB),该模型将通过考虑单个协变量来提供缺陷计数的监视。此外,将建议的基于模型的图表与现有的基于数据的图表进行比较。该模拟研究旨在通过运行长度属性来获取监测方法的性能,并且还提供了一项有关2012-2014年亚特兰大和奥兰多之间航班延误数量的案例研究,以突出上述研究的重要性。将建议的基于模型的图表与现有的基于数据的图表进行比较。该模拟研究旨在通过运行长度属性来获取监测方法的性能,并且还提供了一项有关2012-2014年亚特兰大和奥兰多之间航班延误数量的案例研究,以突出上述研究的重要性。将建议的基于模型的图表与现有的基于数据的图表进行比较。该模拟研究旨在通过运行长度属性来获取监测方法的性能,并且还提供了一项有关2012-2014年亚特兰大和奥兰多之间航班延误数量的案例研究,以突出上述研究的重要性。
更新日期:2020-03-03
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