Linear Profile Monitoring in the Presence of Non- Normality and Autocorrelation

In an increasing number of practical situations, the quality of a process or product can be effectively characterized and summarized by a profile. a profile is usually a functional relationship between a response variable and one or more explanatory variables which can be modeled frequently using linear or nonlinear regression models. in this paper, we study the effect of non-normality on profile monitoring in phase ii when within or between autocorrelation is present. different levels of autocorrelation and skewed and heavy-tailed symmetric nonnormal distributions are used in our study to evaluate the performance of three existing monitoring schemes numerically. simulation results indicate that the non-normality and autocorrelation can have a significant effect on the in-control performances of the considered schemes. results also indicate that the out-of-control performances of the schemes are not very sensitive to low and moderate levels of autocorrelation in moderate and large shifts.