Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Statistical Process Control (SPC) is a statistical method for monitoring variability of processes. Process variation can be categorized as common cause and special cause. Common causes are the natural or expected variation of some change in the process. The presence of a special cause indicates that the process is not in a state of statistical control. The SPC methodology dictates that a search should be initiated when a special cause is detected. This thesis is about the set-up of magnitude robust control chart and CUSUM charts for detecting changes in Weibull processes. The research includes the comparison of the ARL performance of the control charts.
Statistical Process Control, Weibull Distribution, Magnitude Robust Control Chart, CUSUM Chart, ARL, Maximum Likelihood Estimates, Maximum Likelihood Ratio Test
Date of Defense
Date of Defense: October 31, 2008.
Submitted Note
A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Publisher
Florida State University
Identifier
FSU_migr_etd-0537
Zhang, M. (2009). Performance of Control Charts for Weibull Processes. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-0537
