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12.06.13
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Dr. Nien fan Zhang (Ph. D.)
National Institute of Standards and
Technology, USA
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Statistical process control charts for autocorrelated data.
Abstract
Statistical process control (SPC) techniques are widely used in industry for process monitoring and quality improvement. Various statistical control charts have been developed to monitor the process mean and variance. Traditionally SPC methodology is based on a fundamental assumption that process data are statistically independent. Process data, however, are not always statistically independent from each other. In the continuous industries such as the chemical industry, most process data are autocorrelated. Many statisticians and statistical process control practitioners have found that autocorrelation in process data has impact on the performance of the traditional SPC charts. In this talk, we will discuss the effect of process standard deviation estimator on control charts when data are autocorrelated. To accommodate autocorrelated data, we will discuss the EWMAST chart, which is used to detect mean change and the EWMS chart to detect variance change for autocorrelated data.
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