ITM Web Conf.
Volume 36, 2021The 16th IMT-GT International Conference on Mathematics, Statistics and their Applications (ICMSA 2020)
|Number of page(s)||11|
|Section||Statistics and Data Science|
|Published online||26 January 2021|
Change point detection in process control with robust individuals control chart
Department of Mathematical and Actuarial Sciences, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor, Malaysia
2 Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia
3 Department of Agro-Based Industry, Faculty of Agro-Based Industry, Universiti Malaysia Kelantan, Jeli Campus, 17600 Jeli, Kelantan, Malaysia
* Corresponding author: firstname.lastname@example.org
It is crucial to realize when a process has changed and to what extent it has changed, then it would certainly ease the task. On occasion that practitioners could determine the time point of the change, they would have a smaller search window to pursue for the special cause. As a result, the special cause can be discovered quicker and the necessary actions to improve quality can be triggered sooner. In this paper, we had demonstrated the use of so-called exploratory data analysis robust modified individuals control chart incorporating the M-scale estimator and had made some comparisons to the existing charts. The proposed modified robust individuals control chart which incorporates the M-scale estimator in order to compute the process standard deviation offers substantial improvements over the existing median absolute deviation framework. With respect to the application in real data set, the proposed approach appears to perform better than the typical robust control chart, and outperforms other conventional charts particularly in the presence of contamination. Thus, it is for these reasons that the proposed modified robust individuals control chart is preferred especially when there is a possible existence of outliers in data collection process.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.