O efeito da autocorrelação no desempenho dos gráficos de X e R e dos gráficos de X e S²

Control charts are very important tools in statistical quality control of industrial processes and its use started last century. Since its development, the charts have always been attributed to independent processes, i.e. without any correlation between samples. But nowadays, with the high level of automation in the industrial environment, it is noticeable the autocorrelation factor between samples. The main Xcharts used in monitoring quality characteristics represented by continuous variables are the mean (X ), amplitude (R) and variance (S²). Therefore, this work aims to analyze the performance of X and R charts and in of X and S² charts with different sample sizes (4 and 5) for monitoring autocorrelated processes. Through computer simulations using the Fortran software and the use of mathematical expressions was possible to obtain data and performance analysis of the detection power charts for independent observations and for autocorrelated observations according to the model AR (1). The results show that the effect of autocorrelation reduces the ability of monitoring the control charts and that, the greater this effect, the slower the chart becomes in misfits signaling