Development Of Robust Memory-Type Charts Under Repetitive Sampling And Triple Sampling Charts For The Gamma Process - Mahmood, Yasar (2024)
Production processes in modern industries usually produce products with small variations due to technological advancement. The Shewhart-type charts are insensitive in detecting small process shifts. By developing memory-type and adaptive-type charts, researchers have solved the shortcomings of the Shewhart-type charts in detecting small shifts. Also, due to high sampling costs and destructive testing, quality engineers use individual control charts to monitor the process mean. There are three objectives in this thesis. Firstly, the triple exponentially weighted moving average (TEWMA) scheme and Tukey control chart (TCC) are combined to develop the TEWMA-TCC and repetitive sampling (RS) based RS-TEWMA-TCC, to monitor the mean of normal and non-normal distributed processes. The TEWMA-TCC, RS-TEWMA-TCC and competing charts are compared based on average run length (ARL), standard deviation of the run length (SDRL) and median run length (MRL) metrics under both zero-state (ZS) and steady-state (SS) conditions. The TEWMA-TCC and RS-TEWMA-TCC display dominance in detecting mean shifts in both directions. They are also robust to skewed distributions in that they are devoid of the ARL-biased problem. Secondly, the RS for cumulative sum (CUSUM)-type statistics discussed by Riaz et al. (2017) is coupled with the Shewhart chart to propose the RS Shewhart exponentially weighted moving average CUSUM TCC (RS-SEC-TCC).