On designing a new Tukey-EWMA control chart for process monitoring

2015 ◽  
Vol 82 (1-4) ◽  
pp. 1-23 ◽  
Author(s):  
Qurat-Ul-Ain Khaliq ◽  
Muhammad Riaz ◽  
Shabbir Ahmad
Keyword(s):  
Control Chart ◽  
Process Monitoring ◽  
Ewma Control Chart

scholarly journals Multivariate nonparametric chart for influenza epidemic monitoring

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Liu Liu ◽  
Jin Yue ◽  
Xin Lai ◽  
Jianping Huang ◽  
Jian Zhang
Keyword(s):  
Control Chart ◽  
Process Monitoring ◽  
Simulation Study ◽  
Process Variable ◽  
Influenza Epidemic ◽  
Multiple Outcomes ◽  
Multivariate Process ◽  
Ewma Control Chart ◽  
Epidemic Monitoring ◽  
Variable Distribution

AbstractControl chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

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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