New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process

Pramod Dargopatil · V. B. Ghute

18 citations0/0 files storedQuality and Reliability Engineering International

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

Abstract

Abstract In this paper, synthetic T2 chart is developed to monitor bivariate process with correlated variables and autocorrelated observations. The proposed chart is a combination of the Hotelling's T2 chart and the conforming run length chart. The operation and design of the chart are described when observations are autocorrelated and cross correlated. The first‐order vector autoregressive process VAR (1) is used to model the bivariate data from an autocorrelated process of interest. Using an average run length as performance measure criterion in the VAR (1) model, it is observed that autocorrelation seriously impact the performance of the synthetic T2 chart. To reduce the effect of autocorrelation on the performance of the synthetic T2 chart, the skip and mixed sampling strategies are implemented to form rational subgroups in the construction of synthetic T2 chart. The average run length performance of the synthetic T2 chart implementing these strategies is compared with that of the standard strategy of formation of rational subgroups. It is observed that implementing skip and mixed sampling strategies within rational subgroup improves the performance of the synthetic T2 chart.

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References

1996Hotelling's T 2 control chart with adaptive sample sizes 2007SPC Procedures for Monitoring Autocorrelated Processes 2010Effect of measurement error and autocorrelation on theX¯chart 2004Multivariate Quality Control Chart for Autocorrelated Processes 2006Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation 2014The Effect of Autocorrelation on the Hotelling T2 Control Chart 2014Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies 2008A Multivariate Synthetic Control Chart for Monitoring Process Mean Vector

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2022Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts 2019Side-sensitive synthetic and runs-rules charts for monitoring AR(1) processes with skipping sampling strategies 2019Shewhart-type monitoring schemes with supplementary w-of-w runs-rules to monitor the mean of autocorrelated samples 2021A new double sampling scheme to monitor the process mean of autocorrelated observations using an AR(1) model with a skip sampling strategy

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