A machine learning assisted truncated adaptive EWMA chart for robust process monitoring

The classical memory-type control charts, Adaptive EWMA (AE) and Truncated Adaptive EWMA (TAE) are chart types whose analytical properties are traditionally derived under normality assumptions, although they can be applied to non-normal data and operate on truncating the impact of extreme values. However, truncation-based methods can possibly fail to respond appropriately to genuine changes in the process. The research is based on a new Hybrid Machine Learning-assisted Truncated Adaptive EWMA (ML-TAE) chart which involves the use of anomaly scores associated with the machine learning models that adjustively weight each new observation before it is added to the EWMA statistic. Monte Carlo method is usedto evaluate the suggested chart in range of scenarios of contamination and varying shifts. According to the given results, the ML-TAE chart shows a consistent better performance in the stability of Average Run Length (ARL) as it keeps the rates of false-alarm lower and allows detecting a moderate shift much faster than the classical models. The validity of the practical use of the proposed method is shown by a real life case study as well regarding contaminated process environment.