A new multivariate variability control chart based on a covariance matrix combination

Alfaro, José Luis; Álvarez, José Fernando Ortega

doi:10.1002/asmb.2412

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…They applied their technique on a real life data set from a real dataset based on the problem of filling bottles on a Pepsi-Cola production line. Alfaro and Ortega (2019) suggested a new chart based on exponentially weighted covariance matrix combination. Riaz et.…”

Section: Introductionmentioning

confidence: 99%

Multivariate control chart for controlling variability – A comparative study

Das

2023

Int. J. Eng. Sci. Tech

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Control chart is an important technique for statistical process control. In most of the practical situations data are collected on more than one characteristic under study. Hence multivariate control chart has drawn the attention of quality control practitioners. For successful implementation of multivariate control chart, two different charts are to be constructed. One is for controlling mean and another is for controlling variance-covariance matrix or ∑ matrix. For effective implementation of control chart for mean, one need to establish that variability is under control. Hence control chart for controlling variance-covariance matrix is playing an important role in multivariate control chart. There are several control charts available in the literature which can be used for controlling ∑ matrix. In this article, we have discussed different such control charts and studied their performance with respect to their ability to detect the shift in the components of ∑ matrix. Out of control average run length (ARL) is considered as a measure for this purpose and the best method to detect the shift have been identified under different conditions.

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Section: Introductionmentioning

confidence: 99%

Multivariate control chart for controlling variability – A comparative study

Das

2023

Int. J. Eng. Sci. Tech

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“…Alfaro and Ortega 13 proposed a monitoring statistic based on the trace of combined sample covariance matrices to control

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. The main advantage of their control chart is that a change in the mean vector does not affect the chart's performance.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the covariance matrix of bivariate processes with the DVMAX control charts

Machado

Quinino

et al. 2021

Appl Stoch Models Bus & Ind

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Two versions of Phase II attribute+variable (DVMAX) control charts are investigated for monitoring the covariance matrix Σ of bivariate processes. Monitoring always starts with an attribute chart employing the Max D control chart and, depending on the outcome, a variable control chart named VMAX chart is run at a second stage to check for process stability. In the first version, denoted as the DVMAX 1 chart, two independent samples are used at the two stages of the same inspection; with the second version, denoted as the DVMAX 2 chart, the same sample is used at both the first and second stage of the same inspection. This approach, based on the implementation of two types of charts, can be designed to be more advantageous than a single variable control chart in terms of detection speed of a shift in the covariance matrix. In general, we conclude that the DVMAX 1 control charts not only shows the best statistical performance but also presents a lower average sampling cost.A numerical example illustrates the implementation of the proposed control charts.

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