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Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter.

Authors :
Malela-Majika, Jean-Claude
Shongwe, Sandile Charles
Aslam, Muhammad
Zhi Lin Chong
Rapoo, Eeva Maria
Source :
Quality & Reliability Engineering International. Dec2021, Vol. 37 Issue 8, p3580-3599. 20p.
Publication Year :
2021

Abstract

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07488017
Volume :
37
Issue :
8
Database :
Academic Search Index
Journal :
Quality & Reliability Engineering International
Publication Type :
Academic Journal
Accession number :
154701750
Full Text :
https://doi.org/10.1002/qre.2935