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MONITORING PROCESS LOCATION AND DISPERSION SIMULTANEOUSLY USING A CONTROL REGION

JANUARY 2022   -  Volume: 97 -  Pages: 71-78

DOI:

https://doi.org/10.6036/10115

Authors:

LI-PANG CHEN - SYAMSIYATUL MUZAYYANAH -
SU-FEN YANG
- BIN WANG - TING-AN JIANG - SHENGJIN GAN

Disciplines:

  • INFORMATION TECHNOLOGY AND KNOWLEDGE (INTELIGENCIA ARTIFICIAL Y SIMULACION )

Downloads:   21

How to cite this paper:  

Received Date :   9 November 2020

Reviewing Date :   16 November 2020

Accepted Date :   14 April 2021

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Key words:
gráfico de control, sin distribución, dispersión y centralidad, EWMA, región de control kernel, estimación de la densidad kernel., distribution-free, dispersion and location, EWMA, kernel control region, kernel density estimation
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

Control charts are effective tools for detecting out-of-control conditions of process parameters in manufacturing and service industries. The development of distribution-free control charts is important in statistical process control when the process quality variable follows an unknown or a non-normal distribution. This research thus proposes to use a distribution-free technology to establish a new control region based on the exponentially weighted moving average median statistic and exponentially weighted moving average interquartile range statistic for simultaneously monitoring the process location and dispersion and further sets up a corresponding new control chart. We compute the out-of-control average run length to evaluate out-of-control detection performance of the proposed control region and also compare the proposed control region with some existing location and dispersion control charts. Results show that our proposed chart always exhibits superior detection performance when the shifts in process location and/or dispersion are small or moderate. The new control region is thus recommended.

Keywords: control chart, distribution-free, dispersion and location, EWMA, kernel control region, kernel density estimation.

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