The X control chart for monitoring process shifts in mean and variance

2012 ◽  
Vol 50 (3) ◽  
pp. 893-907 ◽  
Author(s):  
Mei Yang ◽  
Zhang Wu ◽  
Ka Man Lee ◽  
Michael B.C. Khoo
2014 ◽  
Vol 912-914 ◽  
pp. 1189-1192
Author(s):  
Hai Yu Wang

This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.


2018 ◽  
Vol 7 (1) ◽  
pp. 23-32
Author(s):  
Adestya Ayu Maharani ◽  
Mustafid Mustafid ◽  
Sudarno Sudarno

Water is one of the most important elements for human life, water treatment is done for human consumption and must fulfill the health requirements with the levels of certain parameters. Quality of Water Treatment II is the second water purification installation owned by PDAM Tirta Moedal Semarang City with production capacity of 60 l/s. Variables used in the water treatment process are correlated with each other, so used multivariate control chart. The Multivariate Exponentially Weighted Moving Average control chart is used for monitoring process mean, and the Multivariate Exponentially Weighted Moving Variance control chart is used for monitoring process variability. The variables used are colour, turbidity, organic substance, manganese and the total dissolved solid. MEWMA control chart with λ = 0.5, showed that the process mean is controlled statistically. MEWMV control chart showed that variability is controlled statistically in λ = 0.4, ω = 0.2 and L = 3.3213. MEWMA and MEWMV control chart showed that the process is not capable because it obtained the value of process capability index less than 1. Keywords: Water, Multivariate Exponentially Weighted Moving Average, Multivariate Exponentially Weighted Moving Variance, process capability.


Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 742 ◽  
Author(s):  
Aslam ◽  
Bantan ◽  
Khan

The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts.


2011 ◽  
Vol 132 (2) ◽  
pp. 303-314 ◽  
Author(s):  
Yanjing Ou ◽  
Zhang Wu ◽  
Thong Ngee Goh

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