用于位置和比例联合监测的新型 Lepage 控制图

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-09-29 DOI:10.1016/j.cie.2024.110614
Shubham R. Shinde , Shashibhushan B. Mahadik , Athanasios C. Rakitzis
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引用次数: 0

摘要

本文介绍了两种新型 Shewhart Lepage 控制图,用于联合监控连续过程的位置和规模参数。第一个控制图基于 van der Waerden (VW) 和 Ansari-Bradley (AB) 测试,而另一个则基于 VW 和 Mood 测试。在流程输出的四种分布模型(即正态分布、对数正态分布、拉普拉斯分布和逻辑分布)下,以平均运行时长(ARL)为单位,对这些图表的统计性能进行了数值评估,并与现有文献中的两个竞争图表进行了比较。数值结果表明,无论过程分布如何,在所有四种图表中,其中一种拟议图表(基于 VW 和 Mood 检验)在检测所有大小参数的同时偏移以及在检测所有大小尺度中未伴随位置偏移(仅尺度偏移)的尺度偏移方面性能最佳。此外,在检测所有尺寸的同时偏移和仅尺度偏移方面,另一种拟议图表(基于 VW 和 AB 检验)的表现优于现有文献中的一种竞争图表。此外,在检测由大的位置偏移和小的尺度偏移组成的同步偏移时,它的表现优于另一个竞争图表,但在检测由小的位置偏移和任意大小的尺度偏移组成的同步偏移时,以及在检测所有大小的唯一尺度偏移时,它的表现不如另一个竞争图表。在检测各种规模的仅位置偏移方面,所有四个图表的表现几乎相似。我们还为所提出的图表提供了回归模型,以便于在实践中进行设计。最后,通过一个真实数据示例说明了建议图表的实际应用。
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New Lepage-type control charts for joint monitoring of location and scale
This paper introduces two new Shewhart Lepage-type control charts for jointly monitoring the location and scale parameters of continuous processes. The first is based on the van der Waerden (VW) and Ansari-Bradley (AB) tests while the other is on the VW and Mood tests. Statistical performances, in terms of average run length (ARL), of these charts are evaluated numerically and compared with that of their two competitor charts in the existing literature under four distributional models for the process output, namely, the normal, lognormal, Laplace, and logistic. The numerical results show that irrespective of the process distribution, one of the proposed charts (based on VW and Mood tests) has the best performance among all the four charts in the detection of simultaneous shifts in both the parameters of all sizes as well as in the detection of shifts in the scale that have not been accompanied by shifts in the location (only-scale shifts) of all sizes. Moreover, the other proposed chart (based on VW and AB tests) performs better than one of the competitor charts in the existing literature for detecting simultaneous shifts as well as only-scale shifts of all sizes. In addition, it performs better than the other competitor chart for detecting the simultaneous shifts consisting of a large location shift and a small scale shift, however, performs worse than that for detecting the simultaneous shifts consisting of a small location shift and a scale shift of any size, as well as for detecting the only-scale shifts of all sizes. All the four charts perform almost similarly in the detection of only-location shifts of all sizes. Regression models are provided for the proposed charts, which facilitate their designs in practice. Finally, the practical implementation of the proposed charts is illustrated through a real-data example.
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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