Statistical Process Control of Stationary Autocorrelated Process
When control charts are used to monitor a process, a standard assumption is that observations from the process at different times are independent random variables. However, the independence assumption is often not reasonable for processes of interest in many applications because the dynamics of the process product autocorrelation in the process observations. The presence of significant autocorrelation in the process observations can have a large impact on traditional control charts developed under the independence assumption. A method of monitoring little shifts in stationary autocorrelated process is discussed in this paper. At first, autoregressive moving-average model is used to fit stationary autocorrelated process. Then, process autocorrelation can be removed by residual method, and exponentially weighted moving average charts are constructed to monitor little shifts of process mean and variance. Comparing with other methods, we can illustration that this EWMA residuals charts have better efficiency for stationary autocorrelated processes.
Auto-regressive moving-average model Residuals Exponentially weighted moving average (EWMA) chart
Wang Haiyu
Zhongyuan University of Technology, Zhengzhou, 450007, China
国际会议
长沙
英文
2748-2751
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）