Enhanced adaptive multivariate EWMA and CUSUM charts for process mean

Abdul Haq · Michael B. C. Khoo · Ming Ha Lee · Saddam Akber Abbasi

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Management Systems and Quality Improvement

Abstract

The multivariate charts are mostly used to simultaneously monitor several quality characteristics in manufacturing processes. In this study, we enhance the sensitivities of the recently proposed adaptive multivariate EWMA (AME) and weighted adaptive multivariate CUSUM (WAMC) charts with an auxiliary-information-based (AIB) estimator, namely the AIB-AME and AIB-WAMC charts, for monitoring different kinds of shifts in the mean of a multivariate normally distributed process. In addition, the variable sampling interval (VSI) feature is also incorporated into the proposed charts. The run length properties of these control charts are computed using Monte Carlo simulations. It is found that the AIB-AME and AIB-WAMC charts are uniformly and substantially more sensitive than the AME and WAMC charts, respectively. The same trend is observed when these control charts have the VSI feature incorporated into them. Real datasets are used to demonstrate the implementation of the proposed charts.

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References

1992A Multivariate Exponentially Weighted Moving Average Control Chart 1988Multivariate Generalizations of Cumulative Sum Quality-Control Schemes 2000Kollár Ádám Ferenc levelezése 1990Comparisons of MultivariateCUSUMCharts 1985Multivariate CUSUM Quality-Control Procedures 1987A Note on Multivariate CUSUM Procedures 1990CUSUM Charts With Variable Sampling Intervals 2001A Rank-Based Multivariate CUSUM Procedure

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2022Economic-statistical design of fully adaptive multivariate control charts under effects of multiple assignable causes 2023A new scheme of dispersion charts based on neoteric ranked set sampling 2022An adaptive multivariate EWMA mean chart with variable sample sizes and/or variable sampling intervals

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