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

Version 2 2024-06-04, 04:36
Version 1 2017-04-07, 14:21
journal contribution
posted on 2024-06-04, 04:36 authored by NGT Gunaratne, MA Abdollahian, Shamsul HudaShamsul Huda, John YearwoodJohn Yearwood
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.

History

Journal

International journal of production research

Volume

55

Pagination

4948-4962

Location

Abingdon, Eng.

ISSN

0020-7543

eISSN

1366-588X

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2017, Informa UK

Issue

17

Publisher

Taylor & Francis