Robust Holt-Winter based control chart for monitoring autocorrelated simple linear profles with contaminated data

Profle monitoring is a useful technique in statistical process control used when quality of the product or process is represented by a function over a time period. This function represents the relationship between a response variable and one or more explanatory variables. Most existing control charts for monitoring profles are based on the assumption that the observations within each profle are independent of each other, which is often violated in practice. Sometimes there are one or more outliers in each profle which lead to poor statistical performance of the control chart. This paper focuses on Phase II monitoring of a simple linear profle with autocorrelation within profle data in the presence of outliers. In this paper, we propose a new combined control chart based on the robust Holt-Winter model to decrease the effect of outliers. We frst evaluate the effect of outliers on the performance of the proposed combined control chart. Then, we apply robust Holt-Winter and design a robust combined control chart to overcome the effect of outliers. The performance of the proposed robust Holt-Winter control chart is evaluated through extensive simulation studies. The results show that the proposed robust control chart performs well.