Abstract
The main goal of this paper is to implement Statistical Process Control (SPC) with Residual Control Charts. In real life applications, such as in industries or manufacturing, many processes are described by autocorrelated observations. In addition to autocorrelation, some types of industrial processes also exhibit a particular kind of trend behavior. A typical example of process with autocorrelations arises in chemical processes. One of the main characteristics of a control chart are the Average Run Length (ARL0) (mean of false alarm times) and the Average Delay time (ARL1) (mean delay of true alarm times). The main parameter observed for a given process is the mean shift. If a process has no mean shift, then the ARL0 should be sufficiently large. On the opposite side, when a mean shift occurs the ARL1 should be small in order to indicate the mean shift quickly. We compared the performance of the Shewhart x, EWMA, and GMA residual control charts for autocorrelation observations with upward/downward linear trend. These comparisons are made for different magnitudes of the mean shift and various levels of autocorrelation. AMS Subject Classification: 60A05
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Pure and Apllied Mathematics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
