Information visualization about changes of process mean and variance on (¯x, s) control chart

2018 ◽  
Vol 16 (4) ◽  
pp. 496-510 ◽  
Author(s):  
Yasuhiko Takemoto ◽  
Ikuo Arizono
Keyword(s):  
Control Chart ◽  
Information Visualization ◽  
Process Mean ◽  
Mean And Variance

Monitoring the process mean and variance using a synthetic control chart with two-stage testing

2009 ◽  
Vol 47 (18) ◽  
pp. 5067-5086 ◽  
Author(s):  
Antonio F. B. Costa ◽  
Maysa S. de Magalhães ◽  
Eugenio K. Epprecht
Keyword(s):  
Control Chart ◽  
Synthetic Control ◽  
Process Mean ◽  
Two Stage ◽  
Mean And Variance

MONITORING AUTOCORRELATED PROCESS MEAN AND VARIANCE USING A GWMA CHART BASED ON RESIDUALS

2008 ◽  
Vol 25 (06) ◽  
pp. 781-792 ◽  
Author(s):  
SHEY-HUEI SHEU ◽  
SHIN-LI LU
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Autoregressive Process ◽  
Process Mean ◽  
Process Variability ◽  
Autocorrelated Process ◽  
Ewma Control Chart ◽  
Mean And Variance ◽  
Run Lengths

This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

Combined Charts for the Mean and Variance

2014 ◽  
Vol 909 ◽  
pp. 352-355 ◽  
Author(s):  
Hyo Il Park
Keyword(s):  
Likelihood Ratio ◽  
Control Chart ◽  
Control Charts ◽  
Process Mean ◽  
Individual Test ◽  
Combined Control ◽  
Mean And Variance ◽  
The Mean ◽  
Intersection Test ◽  
Likelihood Ratio Statistics

In this study, we consider to propose combined control charts for the process mean and variance to control simultaneously. First of all, we review the existing combined control charts and then proposed new control charts based on the union-intersection test for the jointly likelihood ratio statistics. Also we adopt the Liptak combining function for another combined control chart. For those combined control charts, we use the fact that a series of the hypothesis tests is equivalent to the maintenance of control charts. In order to combine the two individual tests for the respective mean and variance, we utilize the p-values for each individual test. Then we discuss some interesting aspects of the combining charts.

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