A Multivariate Synthetic Control Chart for Monitoring Process Mean Vector

Ghute, V. B.; Shirke, D. T.

doi:10.1080/03610920701824265

Cited by 40 publications

(31 citation statements)

References 15 publications

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“…Costa et al (2009) evaluated the properties of a synthetic chart with twostage testing that is able to signal changes in the process mean and in the process variance. Ghute and Shirke (2008) were the first to combine the Hotelling's T 2 and CRL charts. Subsequently, Aparisi and de Luna (2009) developed a synthetic T 2 chart that does not detect shifts in a region of admissible shifts but that detects only important shifts.…”

Section: Introductionmentioning

confidence: 99%

Some Comments Regarding the Synthetic Chart

Machado

Costa

2014

Communications in Statistics - Theory and Methods

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The steady-state average run length (ARL) is a function of the in-control probabilities of being in each nonabsorbing state. Davis and Woodall (2002) tabulated values that are significantly smaller than the steady-state ARLs, because they used the out-of-control probabilities. The synthetic chart signals when a second sample point falls beyond the control limits, no matter whether one of them falls above the centerline and the other falls below it. The side-sensitive version of the synthetic chart does not signal when the points beyond the control limits are on opposite sides. With this rule, the chart detects mean changes more quickly.

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Section: Introductionmentioning

confidence: 99%

Some Comments Regarding the Synthetic Chart

Machado

Costa

2014

Communications in Statistics - Theory and Methods

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“…In MV-Syn-M control chart, for the above problem, Ghute and Shirke [9] computed optimum design parameters (k, L) for given ( ) …”

Section: The Multivariate Synthetic Control Chart For Mean Vector (Mvmentioning

confidence: 99%

“…The development of the synthetic control chart for a univariate process has been also documented by Calzadaand Scariano [4], Davis and Woodall [5], Scariano and Calzada [6], Huang and Chen [7], and Costa and Rahim [8]. The development of the Multivariate synthetic control chart for monitoring process Mean vector (MV-Syn-M) has been proposed by Ghute and Shirke [9]. This chart is developed as a combination of the Hotelling's T 2 chart and the CRL chart.…”

Section: Introductionmentioning

confidence: 99%

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

Gadre¹,

Kakade²

2016

OJS

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In this article, we propose two control charts namely, the "Multivariate Group Runs' (MV-GR-M)" and the "Multivariate Modified Group Runs' (MV-MGR-M)" control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control "Average Time to Signal" (ATS) in the zero state, as well in the steady state compared to the Hotelling's T 2 and the synthetic T 2 control charts.

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“…Introduction he multivariate control chart techniques have received considerable attention in the last couple decades. Ghute and Shirke [9] presented a multivariate synthetic control chart consisted of two sub charts: a sub-chart and a CRL (conforming run length) sub-chart. They pointed out that the substantial improvement in the reduction of ARL should justify its application.…”

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confidence: 99%

The Economic Design of MultivariateMSEControl Chart

Smiley

Mao

2011

Quality Technology & Quantitative Management

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In this paper, the economic design of MSE control chart is extended to the multivariate case.The important feature of this control chart is that it uses the target value instead of the process mean. According to Taguchi's viewpoint, any deviation from the target value represents a kind of loss. Therefore, we construct the model of economic design by considering not only the control costs occurred in the production process but also the loss resulted to the customer because the quality characteristics shifted from the target value. The expected loss of multivariate squared error is presented and used in the formulated cost model. A True Basic program is used to find the optimum parameters of the sample size, the sample interval, and the width, ; n h , E of the control limits of the multivariate MSE chart. Finally, an example is used to illustrate the application of the proposed economic design of the multivariate MSE control chart.

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A Multivariate Synthetic Control Chart for Monitoring Process Mean Vector

Cited by 40 publications

References 15 publications

Some Comments Regarding the Synthetic Chart

Some Comments Regarding the Synthetic Chart

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

The Economic Design of MultivariateMSEControl Chart

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