Authors:
Sheng Shu Cheng
1
;
Fong-Jung Yu
2
;
Shih-Ting Yang
2
and
Jiang-Liang Hou
3
Affiliations:
1
Yu-Da University of Science and Technology, Taiwan
;
2
Nanhua University, Taiwan
;
3
National Tsing Hua University, Taiwan
Keyword(s):
Statistical Process Control, Exponentially Weighted Moving Average, Smoothing Parameter Selection, Determination of the Control Limits.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In the Statistical Process Control (SPC) field, an Exponentially Weighted Moving Average for Stationary processes (EWMAST) chart with proper control limits has been proposed to monitor the process mean of a stationary autocorrelated process. There are two issues of note when using the EWMAST charts. These are the smoothing parameter selections for the process mean shifts, and the determination of the control limits to meet the required average run length (ARL). In this paper, a guideline for selecting the smoothing parameter is discussed. These results can be used to select the optimal smoothing parameter in the EWMAST chart. Also, a numerical procedure using an integration approach is presented for the ARL computation with the specified control limits. The proposed approach is easy to implement and provides a good approximation to the average run length of EWMAST charts.