保证具有估计参数的联合X-S控制图的可接受控制和失控性能

IF 2.3 2区 工程技术 Q3 ENGINEERING, INDUSTRIAL Quality Technology and Quantitative Management Pub Date : 2021-11-01 DOI:10.1080/16843703.2021.1949825
Mosquera Jaime, Aparisi Francisco, Epprecht K. Eugenio
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引用次数: 1

摘要

质量控制图被广泛用于监控生产过程。为了确定控制极限,必须知道被控制的统计量的分布。对于同时使用和S控制图,当参数未知时,必须估计过程的均值和标准差。这些估计中的错误可能导致联合图的实际性能与用户期望的不同,因此控制和失控的ARL可能与理论值相差很大。迄今为止,文献中只研究了估计误差对联合图控制性能的影响。在本文中,我们共同研究了这种影响对两种性能指标的影响,即联合图的控制和失控arl,并估计了第一阶段所需的样本数量,以保证获得具有可接受的控制和失控性能的联合图的概率达到所需水平。
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Guaranteeing acceptable in-control and out-of-control performance of joint X ̅-S control charts with estimated parameters
ABSTRACT Quality control charts are widely used to monitor production processes. To fix the control limits, the distribution of the statistic being controlled must be known. For using and S control charts together, when the parameters are unknown, the mean and standard deviation of the process must be estimated. Errors in these estimates can cause the real performance of the joint charts to be different from that expected by the user, and the in-control and out-of-control ARL may therefore be very different from the theoretical values. To date, only the effect of the estimation errors on the in-control performance of the joint charts has been studied in the literature. In this article, we study jointly this effect on both performance measures, the in-control and out-of-control ARLs for joint charts, and estimate the number of samples needed in Phase I to guarantee a required level in the probability of obtaining joint charts with acceptable in-control and out-of-control performances.
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来源期刊
Quality Technology and Quantitative Management
Quality Technology and Quantitative Management ENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
5.10
自引率
21.40%
发文量
47
审稿时长
>12 weeks
期刊介绍: Quality Technology and Quantitative Management is an international refereed journal publishing original work in quality, reliability, queuing service systems, applied statistics (including methodology, data analysis, simulation), and their applications in business and industrial management. The journal publishes both theoretical and applied research articles using statistical methods or presenting new results, which solve or have the potential to solve real-world management problems.
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