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Optimal design of a nonparametric Shewhart-Lepage control chart

비모수적 Shewhart-Lepage 관리도의 최적 설계

  • Lee, Sungmin (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Jaeheon (Department of Applied Statistics, Chung-Ang University)
  • 이성민 (중앙대학교 응용통계학과) ;
  • 이재헌 (중앙대학교 응용통계학과)
  • Received : 2017.02.20
  • Accepted : 2017.03.13
  • Published : 2017.03.31

Abstract

One of the major issues of statistical process control for variables data is monitoring both the mean and the standard deviation. The traditional approach to monitor these parameters is to simultaneously use two seperate control charts. However there have been some works on developing a single chart using a single plotting statistic for joint monitoring, and it is claimed that they are simpler and may be more appealing than the traditonal one from a practical point of view. When using these control charts for variables data, estimating in-control parameters and checking the normality assumption are the very important step. Nonparametric Shewhart-Lepage chart, proposed by Mukherjee and Chakraborti (2012), is an attractive option, because this chart uses only a single control statistic, and does not require the in-control parameters and the underlying continuous distribution. In this paper, we introduce the Shewhart-Lepage chart, and propose the design procedure to find the optimal diagnosis limits when the location and the scale parameters change simultaneously. We also compare the efficiency of the proposed method with that of Mukherjee and Chakraborti (2012).

전통적인 통계적 공정관리에서 품질특성치의 위치모수와 척도모수의 변화를 탐지하는 것은 주된 관심사였고, 이를 위해 일반적으로 두 개의 관리도를 병행하여 사용한다. 그러나 하나의 관리도를 사용하여 두 모수의 변화를 동시에 탐지하는 절차에 대한 연구도 많이 진행되어 왔다. 하나 또는 두 개의 관리도를 사용할 때, 제1국면 (phase I)을 통하여 모수를 추정하여 관리한계를 설정하여 제2국면(phase II)의 관리도를 운영하는데 이때 정규성 가정의 만족 여부는 아주 중요한 점검 사항이다. 실제 공정에서는 종종 분포에 대한 가정을 하기 어렵거나 정규분포를 따른다고 가정하기 어려운 경우가 있는데, 이러한 경우에는 비모수적 관리도를 사용할 수 있다. 이 논문에서는 비모수적 관리도이면서, 하나의 관리도를 사용하여 위치모수와 척도모수의 변화를 탐지하는 Shewhart-Lepage 관리도를 소개하고, 위치모수와 척도모수가 동시에 변화할 때 진단 단계에서 변화의 원인을 가장 정확하게 진단할 수 있는 최적의 진단한계를 모의실험을 통해 제시하고 그 효율에 대해 연구하였다.

Keywords

References

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