Accurate ARL Calculation for EWMA Control Charts Monitoring Normal Mean and Variance Simultaneously

Knoth, Sven

doi:10.1080/07474940701404823

Cited by 40 publications

(19 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Average run length (ARL) is used to evaluate the performance of any control chart scheme . The ARL measure for the chart performance is studied by many authors including . The MDS sampling scheme has been utilized in the SPC by many authors including Ahmad et al, Aslam et al, Azam et al, Khan et al, and Knoth …”

Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

View full text Add to dashboard Cite

In this article, a new t-chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t-chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in-control average run lengths. The proposed t-chart is compared with the existing chart by using the out-of-control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“… To achieve this, the collocation method proposed by Knoth [23][24] is used to compute the ARL for a fixed control limit of the chart. Details can be found in the Appendix.…”

Section:  mentioning

confidence: 99%

Joint Monitoring Scheme for Clinical Failures and Predisposed Risks

Loke

Gan

2012

Quality Technology & Quantitative Management

View full text Add to dashboard Cite

Measuring quality of medical practice is a key component in improving efficiency in health care.It is becoming increasingly prominent in quality management. At present, risk-adjusted monitoring tools are only used to monitor clinical failures. By using a sensitivity analysis, real life applications and simulated examples, we demonstrate that it is not sufficient to solely monitor clinical failures. In this paper, we propose to jointly monitor clinical failures and predisposed risks of patients. This joint monitoring is not just necessary but also essential to avoid making erroneous inferences based on clinical failures when the predisposed risk distribution has changed. We also proposed a new charting procedure to monitor the risk distribution. The design of the joint monitoring scheme is described in detail by using a real data set.

show abstract

“…Chen et al suggested an MaxEWMA control chart by combining the plotting statistics of the two EWMA control charts—one to detect the process mean and the other to detect changes in the process dispersion. Knoth used the collocation approach to obtain the accurate ARL values of

\overset{X}{true} - S^{2}

EWMA control charts. Sheu et al developed an extended maximum generally weighted moving average (MaxGWMA) control chart to concurrently detect both increases and decreases in the process mean and variability of a process.…”

Section: Introductionmentioning

confidence: 99%

An exponentially weighted moving average chart based on likelihood‐ratio test for monitoring Weibull mean and variance with subgroups

Wang

Cheng

2017

Quality & Reliability Eng

View full text Add to dashboard Cite

Monitoring changes in the Weibull mean and variance simultaneously is of interest in quality control. The mean and variance of a Weibull process are determined by its shape and scale parameters. Most studies are focused on monitoring the Weibull scale parameter with fixed shape parameter or the Weibull shape parameter with fixed scale parameter. In this paper, we propose an exponentially weighted moving average chart based on the likelihood‐ratio test and an inverse error function called ELR chart to monitor changes in the Weibull mean and variance simultaneously. The simulation approach is used to derive the average run length. We compare our proposed chart with other existing control charts for 3 cases, including scale parameter changes with fixed shape parameter, shape parameter changes with fixed scale parameter, and both parameters changes. The results show that the ELR chart outperforms the other control charts in terms of average run length in most cases. Two numerical examples are used to illustrate the applications of the proposed control chart.

show abstract

Accurate ARL Calculation for EWMA Control Charts Monitoring Normal Mean and Variance Simultaneously

Cited by 40 publications

References 15 publications

Design of a t‐chart using generalized multiple dependent state sampling

Design of a t‐chart using generalized multiple dependent state sampling

Joint Monitoring Scheme for Clinical Failures and Predisposed Risks

An exponentially weighted moving average chart based on likelihood‐ratio test for monitoring Weibull mean and variance with subgroups

Contact Info

Product

Resources

About