A loss function-based adaptive control chart for monitoring the process mean and variance

2008 ◽  
Vol 40 (9-10) ◽  
pp. 948-959 ◽  
Author(s):  
Zhang Wu ◽  
Penghui Wang ◽  
Qinan Wang
Keyword(s):  
Adaptive Control ◽  
Control Chart ◽  
Loss Function ◽  
Process Mean ◽  
Mean And Variance ◽  
Adaptive Control Chart

Monitoring linear profiles using an adaptive control chart

2015 ◽  
Vol 82 (5-8) ◽  
pp. 1433-1445 ◽  
Author(s):  
Maysa S. De Magalhães ◽  
Rodrigo Otávio S. Von Doellinger
Keyword(s):  
Adaptive Control ◽  
Control Chart ◽  
Linear Profiles ◽  
Adaptive Control Chart

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.

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