Process monitoring is very important for industries. Statistical methodologies have been used to monitor various production process successfully. The majority of statistical process monitoring techniques assume that the process data are free of autocorreation. However, this assumption isfrequently invalid in many manufacturing processes and other processes. Under such conditions, the traditional statistical monitoring methodologies are not effective. The impetus of this project is to develop new statistical methodlogies to apply to the autocorrelated data.
Develop statistical approaches to monitor process mean, process variability, and process capability indices for autocorrelated data.
Created September 19, 2010, Updated August 31, 2016