An efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale

2020 ◽  
Vol 90 (13) ◽  
pp. 2329-2353 ◽  
Author(s):  
Zhi Song ◽  
Amitava Mukherjee ◽  
Jiujun Zhang
Keyword(s):  
Moving Average ◽  
Initial Response ◽  
Exponentially Weighted Moving Average ◽  
Distribution Free ◽  
Efficient Approach ◽  
Joint Monitoring ◽  
Weighted Moving Average ◽  
Exponentially Weighted

scholarly journals Performance of Joint Quality Monitoring Schemes under Gaussian distribution

2020 ◽  
Vol 9 (2) ◽  
pp. 335-340
Keyword(s):  
Moving Average ◽  
Statistical Quality Control ◽  
Exponentially Weighted Moving Average ◽  
Cumulative Sum ◽  
Monitoring Scheme ◽  
Mean And Variance ◽  
Joint Monitoring ◽  
Mean Variance ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Jointly monitoring the process mean and variance has become a well-known topic in statistical quality control literature after it is considered as a bivariate problem. Many joint monitoring schemes have been proposed by using the Shewhart, cumulative sum and exponentially weighted moving average techniques. In this paper, best performing schemes from each technique has been selected and compared for their performance using average run length properties. It was found that selection of better joint monitoring scheme based on the shift in mean and variance to be detected quickly. In particular, the Shewhart distance joint monitoring scheme performs well when there is larger shifts in mean, variance or in both. In addition, the Shewhart distance joint monitoring scheme performs specific when there is no shift in mean and decrease in variance. For the smaller shifts in mean, variance or in both, cumulative sum and exponentially weighted moving average joint monitoring schemes can be recommended. At this scenario exponentially weighted moving average joint monitoring scheme performs marginally better than the cumulative sum scheme.

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.

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