A Double Multivariate Exponentially Weighted Moving Average (dMEWMA) Control Chart for a Process Location Monitoring

2012 ◽  
Vol 41 (2) ◽  
pp. 238-252 ◽  
Author(s):  
Saad Alkahtani ◽  
Jay Schaffer
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Process Location ◽  
Weighted Moving Average ◽  
Exponentially Weighted

scholarly journals MONITORING PROCESS LOCATION AND DISPERSION SIMULTANEOUSLY USING A CONTROL REGION

10.6036/10115 ◽  
2022 ◽  
Vol 97 (1) ◽  
pp. 71-78
Author(s):  
Li-Pang Chen ◽  
Syamsiyatul Muzayyanah ◽  
SU-FEN YANG ◽  
Bin Wang ◽  
Ting-An Jiang ◽  
...  
Keyword(s):  
Control Chart ◽  
Control Region ◽  
Control Charts ◽  
Moving Average ◽  
Detection Performance ◽  
Exponentially Weighted Moving Average ◽  
Distribution Free ◽  
Process Location ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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.

scholarly journals PENERAPAN DIAGRAM KONTROL MEWMA DAN MEWMV PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Kualitas Pengolahan Air II PDAM Tirta Moedal Kota Semarang)

2018 ◽  
Vol 7 (1) ◽  
pp. 23-32
Author(s):  
Adestya Ayu Maharani ◽  
Mustafid Mustafid ◽  
Sudarno Sudarno
Keyword(s):  
Water Treatment ◽  
Control Chart ◽  
Moving Average ◽  
Process Capability ◽  
Human Consumption ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Monitoring Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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.

scholarly journals Design of SN2-NEWMA Control Chart for Monitoring Process having Indeterminate Production Data

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 742 ◽  
Author(s):  
Aslam ◽  
Bantan ◽  
Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Control Chart ◽  
Moving Average ◽  
Logarithmic Transformation ◽  
Production Data ◽  
Exponentially Weighted Moving Average ◽  
Monitoring Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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.

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