Using one EWMA chart to jointly monitor the process mean and variance

Two control charts are usually used to monitor the process mean and variance separately. The mean is monitored using the $${\bar{{X}}}$$ chart while the variance using either the standard deviation, S chart, or the range, R chart. Recently, numerous single variable charts are proposed to jointly monitor the mean and variance. Most approaches transform the sample mean and sample variance into two statistics, each having a standard scale, and either plotting them on the same chart or combining them into a single statistic to be plotted on a chart. The R chart is more widely used than the S chart but no attempt is made to combine the $${\bar{{X}}}$$ and R charts in the construction of a single variable chart and to study its properties and performance. In this paper, we transform the $${\bar{{X}}}$$ and R statistics into two standard normal random variables, used in the computation of two corresponding exponentially weighted moving average (EWMA) statistics, which are then merged into a single plotting statistic for the proposed chart, called the EWMA $${\bar{{X}}-R}$$ chart.

[1]  Roger G. Schroeder,et al.  A Simultaneous Control Chart , 1987 .

[2]  Zhang Wu,et al.  Monitoring the process mean and variance using a weighted loss function CUSUM scheme with variable sampling intervals , 2006 .

[3]  Smiley W. Cheng,et al.  A New EWMA Control Chart for Monitoring Both Location and Dispersion , 2004 .

[4]  Antonio Fernando Branco Costa,et al.  A Single EWMA Chart for Monitoring Process Mean and Process Variance , 2006 .

[5]  K. E. Case,et al.  Development and Evaluation of Control Charts Using Exponentially Weighted Moving Averages , 1989 .

[6]  Smiley W. Cheng,et al.  Monitoring Process Mean and Variability with One EWMA Chart , 2001 .

[7]  Rickie J. Domangue,et al.  Some omnibus exponentially weighted moving average statistical process monitoring schemes , 1991 .

[8]  Smiley W. Cheng,et al.  Semicircle Control Chart for Variables Data , 1996 .

[9]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[10]  F. Gan Joint monitoring of process mean and variance using exponentially weighted moving average control charts , 1995 .

[11]  António Pacheco,et al.  On the performance of combined EWMA schemes for μ and σ: a markovian approach , 2000 .

[12]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[13]  Joseph F. Daly,et al.  On the Use of the Sample Range in an Analogue of Student's $t$-Test , 1946 .

[14]  Arthur B. Yeh,et al.  Unified CUSUM Charts for Monitoring Process Mean and Variability , 2004 .

[15]  Yu Tian,et al.  Weighted-loss-function control charts , 2006 .

[16]  Smiley W. Cheng,et al.  MAX CHART: COMBINING X-BAR CHART AND S CHART , 1998 .

[17]  Marion R. Reynolds,et al.  Control Charts and the Efficient Allocation of Sampling Resources , 2004, Technometrics.

[18]  Smiley W. Cheng,et al.  Single Variables Control Charts: an Overview , 2006, Qual. Reliab. Eng. Int..

[19]  Dov Ingman,et al.  Adaptive Control Limits for Bivariate Process Monitoring , 1996 .

[20]  Antonio Fernando Branco Costa,et al.  Monitoring Process Mean and Variability with One Non-central Chi-square Chart , 2004 .

[21]  Fah Fatt Gan,et al.  Interval Charting Schemes for Joint Monitoring of Process Mean and Variance , 2004 .