Open AccessAn enhanced nonparametric EWMA sign control chart using sequential mechanism
Author(s) -
Muhammad Riaz,
Muhammad Abid,
Hafız Zafar Nazir,
Saddam Akber Abbasi
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0225330
Subject(s) - ewma chart , control chart , nonparametric statistics , chart , computer science , step detection , process (computing) , control limits , sampling (signal processing) , statistics , sign (mathematics) , mathematics , filter (signal processing) , computer vision , operating system , mathematical analysis
Control charts play a significant role to monitor the performance of a process. Nonparametric control charts are helpful when the probability model of the process output is not known. In such cases, the sampling mechanism becomes very important for picking a suitable sample for process monitoring. This study proposes a nonparametric arcsine exponentially weighted moving average sign chart by using an efficient scheme, namely, sequential sampling scheme. The proposal intends to enhance the detection ability of the arcsine exponentially weighted moving average sign chart, particularly for the detection of small shifts. The performance of the proposal is assessed, and compared with its counterparts, by using some popular run length properties including average, median and standard deviation run lengths. The proposed chart shows efficient shift detection ability as compared to the other charts, considered in this study. A real-life application based on the smartphone accelerometer data-set, for the implementation of the proposed scheme, is also presented.