Controlling over-adjusted process means and variances using VSI cause selecting control charts

Yang, Su‐Fen; Chen, Wan-Yun

doi:10.1016/j.eswa.2008.08.064

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…Yang and Su proposed the VSS

Z_{\overset{x}{true}}

and Z ē , VSI

Z_{\overset{x}{true}}

and Z ē , and VSSI

Z_{\overset{x}{true}}

and Z ē charts to monitor the process means on the dependent first process step and second process step, respectively. Yang and Chen proposed the joint VSI

Z_{\overset{x}{true}}

–

Z_{s_{x}^{2}}

and Z ē –

Z_{s_{e}^{2}}

to monitor both the processes means and variances while Yang and Chen and Yang proposed the same scheme to monitor the processes means and variances when the process may be unnecessarily adjusted.…”

Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

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Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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Statistical quality control is a very useful tool in the hands of business in order to improve their products quality. The control charts are the most widely used and the most effective tool of the statistical quality control. In this paper, the substantial recent developments in the design of the adaptive control charts, focusing on the univariate control charts, which allow some of their parameters to change during production, are presented.

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“…Yang and Su proposed the VSS

Z_{\overset{x}{true}}

and Z ē , VSI

Z_{\overset{x}{true}}

and Z ē , and VSSI

Z_{\overset{x}{true}}

and Z ē charts to monitor the process means on the dependent first process step and second process step, respectively. Yang and Chen proposed the joint VSI

Z_{\overset{x}{true}}

–

Z_{s_{x}^{2}}

and Z ē –

Z_{s_{e}^{2}}

Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

“…However, we may be interested in detecting shifts in both the process mean and variance. Yang and Chen and Yang proposed the joint VSI

Ζ_{\overset{X}{true}} - Z_{S^{2}}

and

Ζ_{\overset{e}{true}} - Z_{S_{e}^{2}}

control charts to control the mean and variance of two dependent process steps.…”

Section: Adaptive Shewhart Control Chartsmentioning

confidence: 99%

Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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show abstract

“…For the adaptive approach in variable charts, it has been shown to give substantially faster detection of most process shifts than the traditional charts (see Bai and Lee, 2002;Costa, 1994Costa, , 1999Rahim, 2006, Costa andMagalhaes, 2007;Lina and Chou, 2005;Prabhu et al, 1994;Reynolds and Stoumbos, 2001;Yang and Chen, 2009;Yang and Rahim, 2005;Yang and Su, 2007;Yang et al, 2010;Wu et al, 2007;Zhang and Wu, 2006;Zimmer et al, 1998Zimmer et al, , 2000. For the traditional attributes charts, in the previous works, they are based on the conforming run length (CRL) to detect the out-of-control condition by checking the number of nonconforming unit or nonconformities in a sample (see Bourke, 1991Bourke, , 2001Kaminsky et al, 1992;Wu and Spedding, 2000;Wu et al, 2001a, b).…”

Section: Introductionmentioning

confidence: 98%

Optimal Variable Sample Size and Sampling Interval MSE Chart Based on Improved Square Root Transformation

Cheng

Yang

2013

Communications in Statistics - Simulation and Computation

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In the field of statistical process control (SPC), control charts for attributes are widely used to detect the out-of-control condition by checking the number of nondefective units or nondefective in a sample. In this article, we use the average time to signal (ATS) and the average number of observations to signal (ANOS) to evaluate the performance of the optimal variable sample size and sampling interval (VSSI) improved square root transformation (ISRT) mean square error (MSE) (VSSI ISRT MSE) control chart for attribute data. In addition, this control chart will be used to monitor: (1) the difference between the process mean and the target value, and (2) the process variance shifts. We found that the optimal VSSI ISRT MSE chart performs better than the specific VSSI, the optimal variable sampling interval (VSI), and the fixed parameters (FP) ISRT MSE charts. An example is given to illustrate this new proposed approach.

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“…To simultaneously monitor the process mean and variance, several studies of adaptive control charts have suggested using adaptive rather than fixed control schemes, and other studies have proposed double or single adaptive charts . To enhance the ability of the proposed single FSI WL chart to detect out‐of‐control loss, a variable sampling interval (VSI) control scheme is included in the WL chart.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Diagnosing Process Loss Using a Weighted‐Loss Control Chart

Yang

Lin

2014

Quality & Reliability Eng

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Product and service quality and productivity loss are all crucial competitive factors of companies in numerous industries. The loss function is a popular method for measuring the loss caused by variations in product or service quality. This study proposes a weighted-loss (WL) control chart to monitor the loss variation among manufacturing or service processes. Because loss is caused by a quality variable deviating from its target value, we set up two corresponding control charts to diagnose the sources of out-of-control loss and implement processes to correct these losses. We further present a WL chart using optimal variable sampling intervals (VSIs) in order to detect out-of-control losses faster compared with a WL chart using a fixed sampling interval (FSI). An example then demonstrates and evaluates the ability of the proposed VSI WL control chart to monitor and diagnose out-of-control process loss. Numerical analyses indicate that the optimal VSI WL chart outperforms the FSI WL chart, the joint X and S 2 charts and the VSI Average Loss chart in detecting out-of-control process loss. Thus, the proposed VSI WL chart is recommended.

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Controlling over-adjusted process means and variances using VSI cause selecting control charts

Cited by 15 publications

References 16 publications

Adaptive Control Charts: Recent Developments and Extensions

Adaptive Control Charts: Recent Developments and Extensions

Optimal Variable Sample Size and Sampling Interval MSE Chart Based on Improved Square Root Transformation

Monitoring and Diagnosing Process Loss Using a Weighted‐Loss Control Chart

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