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EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes

Summary: [This abstract is based on the authors' abstract.] A standard assumption when using a control chart to monitor a process is that the observations of output are independent. However, for many processes the observations are autocorrelated. This can have a significant effect on the performance of the control chart. Observations that can be modeled as an AR(1) process plus random error are considered. An exponentially weighted moving average (EWMA) control chart based on the residuals from the forecast values of the model is evaluated using an integral equation method. This control chart's performance is compared to the performance of an EWMA control chart based on the original observations, and the effect of process parameter estimation is investigated. When the level of autocorrelation is low or moderate, the two EWMA charts require about the same time to detect shifts. For high levels of autocorrelation and large shifts, the EWMA chart is a little faster.

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  • Topics:
  • Keywords: Autocorrelation,Autoregression,Residuals,Exponentially weighted moving average control charts (EWMA),Average run length (ARL)
  • Author: Lu, Chao-Wen; Reynolds, Marion R., Jr.
  • Journal: Journal of Quality Technology