scholarly journals Distribution‐free triple EWMA control chart for monitoring the process location using the Wilcoxon rank‐sum statistic with fast initial response feature

2021 ◽  
Author(s):  
Tokelo Irene Letshedi ◽  
Jean‐Claude Malela‐Majika ◽  
Philippe Castagliola ◽  
Sandile Charles Shongwe
Keyword(s):  
Control Chart ◽  
Initial Response ◽  
Distribution Free ◽  
Ewma Control Chart ◽  
Process Location ◽  
Response Feature

Extension of CV-GWMA Control Chart with Fast Initial Response Feature

2012 ◽  
Vol 14 (1) ◽  
pp. 471-476
Author(s):  
Eui Pyo Hong ◽  
Chang Wook Kang ◽  
Hae Woon Kang
Keyword(s):  
Control Chart ◽  
Initial Response ◽  
Response Feature

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 Monitoring process mean with a new EWMA control chart

Production ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 217-222 ◽  
Author(s):  
Yang Su-Fen ◽  
Tsai Wen-Chi ◽  
Huang Tzee-Ming ◽  
Yang Chi-Chin ◽  
Cheng Smiley
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Population Distribution ◽  
Average Run Length ◽  
Process Data ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Simple Statistic ◽  
Run Lengths ◽  
Proper Values

In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length.

Body Welding Quality Control Based on a CUSUM Control Chart

2011 ◽  
Vol 228-229 ◽  
pp. 1080-1084
Author(s):  
Rong Li ◽  
Jing Li ◽  
Jian Liu
Keyword(s):  
Quality Control ◽  
Sample Size ◽  
Control Chart ◽  
Quality Inspection ◽  
Welding Quality ◽  
Ewma Control Chart ◽  
Cusum Control Chart ◽  
Abnormal Variation ◽  
Quality Variation

Aiming at the situation in some Chinese auto companies that the workload of body welding quality inspection is high and the sample size is extremely small, a brand-new CUSUM Control Chart for variance monitoring is proposed in the paper to realize the effective quality control in body welding variance, whose principle is to use variance statistics based on Queensberry transformation Φ-1(G((n-1) St2/σ02)) to monitor infinitely small variances in the process of body welding. Evaluation instance results show that, compared with traditional CUSUM control chart, EWMA control chart and weighted CUSUM control chart, the proposed CUSUM control chart based on variance monitoring is more sensitive to the abnormal variation fluctuation and can detect the abnormity of quality variation earlier.

A EWMA Control Chart for Exponential Distributed Quality Based on Moving Average Statistics

2015 ◽  
Vol 32 (3) ◽  
pp. 1179-1190 ◽  
Author(s):  
Nasrullah Khan ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Ewma Control Chart
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