ABSTRACT Outliers are considered to be the observations that are significantly different than the process distribution. It may seriously damage the performance of traditional Control charts by creating false alarms or hiding actual process changes. This paper therefore suggests a SVR‐assisted Truncated Adaptive EWMA (SVR‐TAE) control chart to enable efficient monitoring of the process in pollutant‐contaminated environments. The suggested solution combines machine learning with dynamic truncation and smoothing to adaptively allocate the weights of observations and lessen the impact of outliers. The Monte Carlo simulations are conducted to assess the performance of the proposed chart with the help of Average Run Length (ARL) and Standard Deviation of Run Length (SDRL). Contaminated situations are the ones in which outliers are created in certain ranges at varying probability of contamination. The findings indicate that the suggested chart can retain the targeted in control ARL and detect small to moderate changes in the mean quicker than the current methods. The real‐life data set has been used to demonstrate the application of the proposed control chart.
Aljohani et al. (Fri,) studied this question.