A comparative study of the median run length (MRL) performance of the Max-DEWMA and SS-DEWMA control charts

Teh, Sin Yin; Khoo, Michael B. C.; Ong, Ker Hsin; Soh, Keng Lin

doi:10.1063/1.4887709

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…It is already well established that DEWMA control charts are more efficient than EWMA control charts in detecting smaller value shifts. Because of certain advantages that they hold over EWMA, DEWMA control charts are frequently used for monitoring processes and products that focus on refining effectiveness of controlling processes while detecting sustainable shifts (eg, Hong et al, Teh et al, and Chao et al).…”

Section: Introductionmentioning

confidence: 99%

Bayesian monitoring of linear profile monitoring using DEWMA charts

Abbas

Qian

Ahmad

et al. 2017

Quality & Reliability Eng

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Process monitoring is an essential element for an improved quality of final products.A variety of tools are used for it; control charts are one of these choices. Classical and Bayesian thoughts are 2 main aspects of statistics used in different areas of application. This study introduces an approach to existing theories in applied quality control:Bayesian double exponentially weighted moving average (DEWMA) control charts for monitoring the profiles of products and processes. Three novel univariate Bayesian DEWMA charting structures for the Y intercepts, slopes, and error variances are designed under phase 2 procedures. The performance of the designed structures of control charts is evaluated based on different run length measures. The comparative analysis revealed that Bayesian DEWMA control charts are efficient at identifying the sustainable shifts in the process parameters. Moreover, DEWMA control charts are more effective under classical and Bayesian methodologies for detecting smaller value shifts compared with exponentially weighted moving average charts. We have examined that acquiring extra information in the form of prior's about process parameters comes up with tangible benefits and enhances the detection potential of DEWMA charts for profiles monitoring. An example and case studies are provided to justify the above findings.

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Section: Introductionmentioning

confidence: 99%

Bayesian monitoring of linear profile monitoring using DEWMA charts

Abbas

Qian

Ahmad

et al. 2017

Quality & Reliability Eng

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“…This value represents the mean of the run-length distribution, which is the number of rational subgroups to be plotted before the monitoring scheme signals for the first time. A number of authors have criticised the sole use of this measure for two main reasons, which are: (i) the value does not give enough information since the run-length distribution is highly skewed, and (ii) the value assesses the performance of a monitoring scheme for a specific shift (Teh et al, 2014;Shongwe & Graham, 2017). In practice, we need to get useful information missing in the criterion and assess the performance over a range of shifts including the overall performance of a monitoring scheme.…”

Section: Performance Measuresmentioning

confidence: 99%

A new distribution-free generally weighted moving average monitoring scheme for detecting unknown shifts in the process location

Mabude¹,

Malela‐Majika²,

Shongwe³

2020

10.5267/j.ijiec

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Distribution-free (or nonparametric) monitoring schemes are needed in industrial, chemical and biochemical processes or any other analytical non-industrial process when the assumption of normality fails to hold. The Mann-Whitney (MW) test is one of the most powerful tests used in the design of these types of monitoring schemes. This test is equivalent to the Wilcoxon ranksum (WRS) test. In this paper, we propose a new distribution-free generally weighted moving average (GWMA) monitoring scheme based on the WRS statistic. The performance of the proposed scheme is investigated using the average run-length, the standard deviation of the runlength, percentile of the run-length and some characteristics of the quality loss function through extensive simulation. The proposed scheme is compared with the existing parametric and nonparametric GWMA monitoring schemes and other well-known control schemes. The effect of the estimated design parameters as well as the effect of the Phase I sample size on the Phase II performance of the new monitoring scheme are also investigated. The results show that the proposed scheme presents better and attractive mean shifts detection properties, and therefore outperforms the existing monitoring schemes in many situations. Moreover, it requires a reasonable number of Phase I observations to guarantee stability and accuracy in the Phase II performance.

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“…Originally not aware of the latter paper, Zhang and Chen 19 also studied the performance of the DEWMA chart to monitor the process mean; note though, there are additional aspects that were considered in the latter paper but absent in the former. Some additional studies on the DEWMA charts in different contexts can be found in Hong et al., 20 Alkahtani, 21 Teh et al., 22 Chao et al., 23 and Alevizakos and Koukouvinos 24 . Based on latter publications, it is generally accepted that the DEWMA charts are more sensitive than EWMA charts for small‐to‐moderate shifts.…”

Section: Introductionmentioning

confidence: 99%

Distribution‐free triple EWMA control chart for monitoring the process location using the Wilcoxon rank‐sum statistic with fast initial response feature

Letshedi

Malela‐Majika

Castagliola

et al. 2021

Quality & Reliability Eng

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The exponentially weighted moving average (EWMA) control chart is a memory‐type chart known to be more efficient in detecting small and moderate shifts in the process parameter. The double EWMA (DEWMA) chart is an extension of the EWMA chart that is more effective than the latter in the detection of small‐to‐moderate shifts. This paper proposes a new distribution‐free (or nonparametric) triple EWMA (TEWMA) control chart based on the Wilcoxon rank‐sum (W) statistic to improve the detection ability in the process location parameter. Moreover, a new fast initial response (FIR) feature is added to further improve the sensitivity of the new TEWMA chart. The performance of the proposed TEWMA chart with and without FIR features is compared to those of the existing EWMA and DEWMA W charts. It is observed that the TEWMA chart with and without FIR features is superior to the competing charts in most situations. A real‐life illustration is provided to show the application and implementation of the new chart.

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A comparative study of the median run length (MRL) performance of the Max-DEWMA and SS-DEWMA control charts

Cited by 4 publications

References 11 publications

Bayesian monitoring of linear profile monitoring using DEWMA charts

Bayesian monitoring of linear profile monitoring using DEWMA charts

A new distribution-free generally weighted moving average monitoring scheme for detecting unknown shifts in the process location

Distribution‐free triple EWMA control chart for monitoring the process location using the Wilcoxon rank‐sum statistic with fast initial response feature

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