Enhancing the Performance of Combined Shewhart‐EWMA Charts

Abujiya, Mu’azu Ramat; Riaz, Muhammad; Lee, Muhammad Hisyam

doi:10.1002/qre.1461

Cited by 53 publications

(50 citation statements)

References 23 publications

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“…We used the average extra quadratic loss ( EQL ) and the relative ARL ( RARL ) to measure the overall performance of the charts. These measure the efficiency of the modified charts over all shifts . The EQL is the weighted average of the ARL over all shifts and uses δ 2 as the weights.…”

Section: Resultsmentioning

confidence: 99%

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

Adegoke

Dawod

Pawley

2018

Quality & Reliability Eng

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When using control charts to monitor manufacturing processes, the exponentially weighted moving average (EWMA) control chart is useful for detecting persistent shifts in the process parameter. This paper proposes enhancements to the applications of the EWMA control chart for those scenarios where the exact measurement of process units is difficult and expensive, but the visual ordering of the units can be done easily. The proposed charts use an auxiliary variable that is correlated with the process variable to provide efficient monitoring of shifts in the process mean and are formulated based on ranked set sampling (RSS) and median RSS schemes (MRSS). Simulation results showed that the proposed charting schemes are more efficient in detecting a shift in the process mean than the classical EWMA control chart and its modification. An example is provided to show the application of the proposed charts using a simulated benchmark process: the continuous stirred tank reactor (CSTR).

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Section: Resultsmentioning

confidence: 99%

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

Adegoke

Dawod

Pawley

2018

Quality & Reliability Eng

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“…The scope of this study can be extended to the design structures of other control charts such as mixed EWMA CUSUM [43], combined Shewhart-EWMA [44], Shewhart-CUSUM [45], and double EWMA charts [46]. Moreover, these ideas may also be investigated for multivariate setups [47].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Ahmad

Riaz

2017

Scientia Iranica

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Abstract. Control charts are popular tounderols for process monitoring and are mainly classi ed into memory and memory-less structures. Exponentially Weighted Moving Average control charts (EWMA) are widely used for the detection of small shifts in process parameters. The traditional design structures of these types of charts rely on the stringent assumption of normality. In practice, quality characteristics generally do not follow normality; hence, alternative charting structures are needed. Nonparametric control chart is one such alternative that do not depend on the mentioned distributional assumption(s). This study deals with nonparametric EWMA charts for an e cient detection of smaller shifts in location. We have investigated asymptotic, time-varying, and Fast Initial Response (FIR) based time varying control limits for nonparametric EWMA charts. We have considered two variants of FIR with time varying limits for nonparametric EWMA structure. A variety of run length properties have been investigated for performance evaluations. It is observed that FIR-based nonparametric EWMA charts with time-varying limits are quite sensitive to detecting smaller shifts, and they o er attractive run length properties. A real-life example is also provided to illustrate the application of the time-varying and FIR-based nonparametric EWMA control charts.

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“…In process monitoring, there is a variety of literature. To refer to but a few of these: cause-selecting and regression-adjusted control charts (Hawkins 1993;Shu, Tsung, and Tsui 2005), auxiliary information-based control charts (Riaz 2008a(Riaz , 2008bRiaz and Does 2009b;Abbasi and Riaz 2013), and ranked set-based charting structures (Abujiya, Riaz, and Lee 2013;Mehmood, Riaz, and Does 2013a). The use of one auxiliary variable may be extended to more variables e.g.…”

Section: Introductionmentioning

confidence: 99%

Control charting and survey sampling techniques in process monitoring

Riaz

2014

Journal of the Chinese Institute of Engineers

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Control charts are widely used for the detection of shifts in process parameters. In this study, we propose some enhanced Shewhart-type control charts using improved estimators based on auxiliary characteristics. A general control charting structure is developed that can be used with different location estimators. Performance of the proposed charts is compared using average run length (ARL), extra quadratic loss (EQL), and relative average run length (RARL) as performance measures. Comparisons are made among different proposals and the existing counterparts. It is observed that the proposed charts offer improved detection abilities depending upon the correlation structures among different variables under consideration. An illustrative example is also included to highlight the applications in practical situations.

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Enhancing the Performance of Combined Shewhart‐EWMA Charts

Cited by 53 publications

References 23 publications

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Control charting and survey sampling techniques in process monitoring

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