Multivariate control charts for monitoring process mean vector of individual observations under regularized covariance estimation

2021 ◽  
pp. 1-22
Author(s):  
Olusola T. Omolofe ◽  
Nurudeen A. Adegoke ◽  
Olatunde A. Adeoti ◽  
Olusoga A. Fasoranbaku ◽  
Saddam Akber Abbasi
Keyword(s):  
Control Charts ◽  
Covariance Estimation ◽  
Process Mean ◽  
Multivariate Control Charts ◽  
Monitoring Process ◽  
Multivariate Control ◽  
Mean Vector

scholarly journals Robust Multivariate Control Charts to Detect Small Shifts in Mean

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Habshah Midi ◽  
Ashkan Shabbak
Keyword(s):  
Control Charts ◽  
Substantial Improvement ◽  
Data Set ◽  
Multivariate Control Charts ◽  
Mean Vectors ◽  
The Mean ◽  
Simulation Results ◽  
Multivariate Control ◽  
Mean Vector

The classical multivariate CUSUM and EWMA charts are commonly used to detect small shifts in the mean vectors. It is now evident that those charts are easily affected by outliers which may be due to small or moderate changes in the mean vector. In this paper, we propose a robust multivariate CUSUM and Robust multivariate EWMA charts to remedy the problem of small changed in scatter outliers. Both the empirical and simulation results indicate that the proposed robust multivariate CUSUM and EWMA charts offer substantial improvement over other multivariate CUSUM and EWMA charts. This article also discussed the robustness of the proposed charts, when there is a small or moderate sustained shift in the data set.

scholarly journals An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

2020 ◽  
pp. 1-7
Author(s):  
Siti Rahayu Mohd Hashim ◽  
Azwaan Andrew ◽  
Wilter Azwal Malandi
Keyword(s):  
Water Quality ◽  
Control Chart ◽  
Control Charts ◽  
Ph Value ◽  
Statistical Process ◽  
Multivariate Control Charts ◽  
Monitoring Process ◽  
Process Dispersion ◽  
Water Treatments ◽  
Multivariate Control

Control chart is a tool for detecting an out-of-control signal in statistical process control (SPC). It is widely used in process monitoring in order to detect changes in process mean or process dispersion. This study aims to illustrate the application of multivariate control charts in monitoring water quality at one of the water treatments plants in Kota Kinabalu, Sabah. The tested water quality variables in this study are turbidity, pH value, dissolved oxygen (DO) and concentration of ferum. Two multivariate control charts, Hotelling’sT2 and MCUSUM control charts are constructed under the violation of the multivariate normality assumption. The purpose is to study the effect of non-normal data upon the monitoring process using the selected multivariate control charts. By comparing the monitoring process between the two types of control charts, the consistency of the results is studied. All the univariate and multivariate control charts produced out-of-control signals from different points, hence inconclusive results obtained. Keywords: Water quality; multivariate control chart; univariate control chart; Hotelling’s T2; MCUSUM

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