Exponentially weighted control charts to monitor multivariate process variability for high dimensions

Gunaratne, N, Abdollahian, M, Huda, S and Yearwood, J 2017, 'Exponentially weighted control charts to monitor multivariate process variability for high dimensions', International Journal of Production Research, vol. 55, no. 17, pp. 4948-4962.


Document type: Journal Article
Collection: Journal Articles

Title Exponentially weighted control charts to monitor multivariate process variability for high dimensions
Author(s) Gunaratne, N
Abdollahian, M
Huda, S
Yearwood, J
Year 2017
Journal name International Journal of Production Research
Volume number 55
Issue number 17
Start page 4948
End page 4962
Total pages 16
Publisher Taylor and Francis
Abstract Multivariate monitoring of industrial or clinical procedures often involves more than three correlated quality characteristics and the status of the process is judged using a sample of size one. Majority of existing control charts for monitoring process variability for individual observations are capable of monitoring up to three characteristics. One of the hurdles in designing optimal control charts for large dimension data is the enormous computing resources and time that is required by simulation algorithm to estimate the charts parameters. This paper proposes a novel algorithm based on Parallelised Monte Carlo simulation to improve the ability of the Multivariate Exponentially Weighted Mean Squared Deviation and Multivariate Exponentially Weighted Moving Variance charts to monitor process variability for high dimensions in a computationally efficient way. Different techniques have been deployed to reduce computing space and execution time. The optimal control limits (L) to detect small, medium and large shifts in the covariance matrix of up to 15 characteristics are provided. Furthermore, utilising the large number of optimal L values generated by the algorithm enabled authors to develop exponential decay functions to predict L values. This eliminates the need for further execution of the parallelised Monte Carlo simulation.
Subject Applied Statistics
Optimisation
Keyword(s) exponential decay function
individual observations
MEWMS
MEWMV
multivariate variability
parallel Monte Carlo simulation
DOI - identifier 10.1080/00207543.2016.1278081
Copyright notice © 2017 Informa UK Limited
ISSN 0020-7543
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 106 Abstract Views  -  Detailed Statistics
Created: Wed, 08 Feb 2017, 08:36:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us