Design of EWMA control chart for monitoring transformed Rayleigh distributed data Online publication date: Thu, 23-Jun-2022
by Olatunde Adebayo Adeoti
International Journal of Quality Engineering and Technology (IJQET), Vol. 8, No. 4, 2022
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Quality Engineering and Technology (IJQET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook