A New Nonparametric EWMA Control Chart for Monitoring Process Variability

2017 ◽  
Vol 33 (7) ◽  
pp. 1499-1512 ◽  
Author(s):  
Abdul Haq
Keyword(s):  
Control Chart ◽  
Process Variability ◽  
Ewma Control Chart ◽  
Monitoring Process

A multivariate EWMA control chart for monitoring process variability with individual observations

2005 ◽  
Vol 37 (11) ◽  
pp. 1023-1035 ◽  
Author(s):  
Arthur B. Yeh ◽  
Longcheen Huwang ◽  
Chien-Wei Wu
Keyword(s):  
Control Chart ◽  
Process Variability ◽  
Ewma Control Chart ◽  
Monitoring Process

scholarly journals Nonparametric Double EWMA Control Chart for Process Monitoring

2016 ◽  
Vol 39 (2) ◽  
pp. 167 ◽  
Author(s):  
Muhammad Riaza ◽  
Saddam Akber Abbasib
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Average Run Length ◽  
Location Parameter ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Run Length ◽  
Ewma Control Chart ◽  
Monitoring Process ◽  
Nonparametric Techniques

<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations.</p>

MONITORING AUTOCORRELATED PROCESS MEAN AND VARIANCE USING A GWMA CHART BASED ON RESIDUALS

2008 ◽  
Vol 25 (06) ◽  
pp. 781-792 ◽  
Author(s):  
SHEY-HUEI SHEU ◽  
SHIN-LI LU
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Autoregressive Process ◽  
Process Mean ◽  
Process Variability ◽  
Autocorrelated Process ◽  
Ewma Control Chart ◽  
Mean And Variance ◽  
Run Lengths

This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

Multiobjective design of an S control chart for monitoring process variability

2012 ◽  
Vol 2 (4) ◽  
pp. 408 ◽  
Author(s):  
Abdul Sattar Safaei ◽  
Reza Baradaran Kazemzadeh ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Control Chart ◽  
Process Variability ◽  
Monitoring Process ◽  
Multiobjective Design
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