Truncated normal distribution-based EWMA control chart for monitoring the process mean in the presence of outliers

In many applications, it is very important to detect outliers during the analysis of normal data. Many existing methods preprocess the data to remove the outliers and then analyse the data accordingly when the data are contaminated by different unexpected outliers; however, it is difficult to use this method for applications where the data must be analysed online. In this article, we present an online monitoring approach for statistical process control (SPC) that is robust with respect to the presence of outliers. The Monte Carlo simulation results show that the proposed control chart is quite robust under the standard normally distributed data and, moreover, the control limit is not be affected by the number and sizes of the outliers. Furthermore, a real data example from a foetal heart rate (FHR) process is used to illustrate an application of our proposed procedure.