scholarly journals An EWMA control chart for the multivariate coefficient of variation

2019 ◽  
Vol 35 (6) ◽  
pp. 1515-1541 ◽  
Author(s):  
Vicent Giner‐Bosch ◽  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Michael Boon Chong Khoo
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Ewma Control Chart ◽  
Multivariate Coefficient Of Variation

A Control Chart for the Multivariate Coefficient of Variation

2015 ◽  
Vol 32 (3) ◽  
pp. 1213-1225 ◽  
Author(s):  
Wai Chung Yeong ◽  
Michael Boon Chong Khoo ◽  
Wei Lin Teoh ◽  
Philippe Castagliola
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Multivariate Coefficient Of Variation

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.

Sign in / Sign up

Export Citation Format

Share Document