A New Combined Shewhart-Cumulative Sum S Chart for Monitoring Process Standard Deviation

2015 ◽  
Vol 32 (3) ◽  
pp. 1149-1165 ◽  
Author(s):  
Mu'azu Ramat Abujiya ◽  
Muhammad Riaz ◽  
Muhammad Hisyam Lee
Keyword(s):  
Standard Deviation ◽  
Cumulative Sum ◽  
Monitoring Process ◽  
Process Standard Deviation

ESTIMATING THE PROCESS STANDARD DEVIATION BASED ON DOWNTON'S ESTIMATOR

2000 ◽  
Vol 12 (3) ◽  
pp. 357-363 ◽  
Author(s):  
Moustafa O. Abu-Shawiesh ◽  
Mokhtar B. Abdullah
Keyword(s):  
Standard Deviation ◽  
Process Standard Deviation

scholarly journals A Combined Upper-Sided Synthetic S2 Chart for Monitoring the Process Variance

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Tingting Shan ◽  
Liusan Wu ◽  
Xuelong Hu
Keyword(s):  
Steady State ◽  
Standard Deviation ◽  
Average Run Length ◽  
Process Standard Deviation ◽  
Distributed Process ◽  
Process Variance ◽  
Synthetic Chart ◽  
And Performance ◽  
Run Rules ◽  
State Average

In order to monitor the process variance, this paper proposes a combined upper-sided synthetic S2 chart for monitoring the process standard deviation of a normally distributed process. This combined upper-sided synthetic S2 chart comprises a synthetic chart and an upper-sided S2 chart. The design and performance of the proposed chart are presented, and the steady-state average run length comparisons show that the combined upper-sided synthetic S2 chart outperforms the standard synthetic S2 chart as well as several run rules S2 charts, especially for larger shifts in the process variance.

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>

Sign in / Sign up

Export Citation Format

Share Document