Design of EWMA control chart for monitoring transformed Rayleigh distributed data. (14th June 2022) - Adeoti, Olatunde Adebayo
Monitoring statistical process for the detection of assignable causes of variation is based on the assumption that the process characteristic follows the normal distribution. But, in practice, this is often not the case as process characteristic seldom follows the non-normal distribution. This paper designs a new control chart to monitor quality characteristic that follow the non-normal distribution. The proposed control chart based on the EWMA statistic is constructed after transforming the Rayleigh distributed data to approximate normal using the power transformation method. The ARL and SDRL values of the proposed control chart are evaluated for different shift sizes. The performance of the proposed chart is compared with the recent CUSUM chart for transformed Rayleigh distributed data. The study shows that the proposed chart outperforms the recent CUSUM control chart for transformed Rayleigh data. Real-life and simulated dataset to illustrate the design and applications of the proposed control chart is given.