Nonparametric triple exponentially weighted moving average signed‐rank control chart for monitoring shifts in the process location

2021 ◽  
Author(s):  
Vasileios Alevizakos ◽  
Kashinath Chatterjee ◽  
Christos Koukouvinos
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Signed Rank ◽  
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.

Enhanced nonparametric control charts under simple and ranked set sampling schemes

2020 ◽  
Vol 42 (14) ◽  
pp. 2744-2759 ◽  
Author(s):  
Zameer Abbas ◽  
Hafiz Zafar Nazir ◽  
Muhammad Abid ◽  
Noureen Akhtar ◽  
Muhammad Riaz
Keyword(s):  
Statistical Process Control ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Sampling Schemes ◽  
Signed Rank ◽  
Process Location ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart.

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.

Sign in / Sign up

Export Citation Format

Share Document