Monitoring the process location by using new ranked set sampling-based memory control charts

Nawaz, Tahir; Han, Dong

doi:10.1080/16843703.2019.1572288

Cited by 33 publications

(25 citation statements)

References 52 publications

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“…Mehmood et al (2013a) extended the detection ability of the ranked set techniques-based mean control chart by adding runs rules. The interested reader can find more studies of control charts involving ranked set schemes in the works of Nazir et al (2013), Mehmood et al (2014), Abbasi and Riaz (2016), Mehmood et al (2017), Hussain et al (2019), Noor-ul Amin and Tayyab (2020), Nawaz and Han (2020), Noor-ul Amin and Riaz (2020), and Koyuncu and Karagoz (2020), and references therein. The aforementioned elaborated control charts deal with several related characteristics independently, and therefore they are called univariate control charts.…”

Section: Introductionmentioning

confidence: 99%

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules

Mehmood

Riaz

Ali

et al. 2021

Transactions of the Institute of Measurement and Control

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In this study, we have introduced a generalized Hotelling T2 control chart based on bivariate ranked set techniques with runs rules to identify small and moderate variations in a process mean vector. To achieve this aim, plotting statistic and control limits are formulated in generalized approaches. For evaluation purposes, power and power curves are used as performance indicators. Afterwards, power curves are drawn through Monte Carlo simulation procedures by taking into account different choices of factors. A detailed discussion about the role of factors on the performance of the proposed generalized control chart is included. Furthermore, the proposed generalized control chart with double bivariate ranked set techniques is noted to be superb compared to the other cases of single bivariate ranked set techniques. Among single and double versions of bivariate ranked set techniques, the proposed generalized control chart on the basis of median bivariate ranked set techniques is recorded as more efficient relative to the other choices under consideration. Also, comparative analysis shows that the proposed generalized control chart with supplementary runs rules performs outstandingly for detection of small and moderate variations relative to existing control charts. Special cases of the proposed generalized control chart are elaborated to highlight its features for accommodating the existing control charts. To amplify the uses and advantages of the proposed generalized control chart, a real-world example from agriculture is presented.

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Section: Introductionmentioning

confidence: 99%

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules

Mehmood

Riaz

Ali

et al. 2021

Transactions of the Institute of Measurement and Control

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“…In the SPCM literature, the homogeneously weighted moving average (HWMA) chart proposed by Abbas 19 became well known. The interested readers can see the work of the following researchers: Adegoke et al, 20,21 Nawaz and Han, 22 Abid et al, 23 and Abbas et al 24 on the HWMA chart under various directions. So, taking motivation from the structures of the HWMA chart, this article suggests a new CUSUM chart by using the statistic of the HWMA chart and from now labeled as the MHC chart.…”

Section: Introductionmentioning

confidence: 99%

A mixed HWMA‐CUSUM mean chart with an application to manufacturing process

Abid

Sun

Nazir

et al. 2020

Quality & Reliability Eng

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Memory‐type control charts play a significant role to identify slight changes in the parameters of the production process. In this article, we have proposed a new cumulative sum chart that utilizes the statistic of the homogeneously weighted moving average chart. The performance of the proposed chart is studied using Monte Carlo simulations. The proposed chart is compared with some existing charts under different run length profiles. The run length profile comparisons reveal that the proposed chart performs superior as compared to the existing control charts. A real‐life application using a manufacturing process dataset is also part of this study.

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“…More recently, Abbas (2018) developed a new memory-type scheme that allocates a specific weight to the current sample and the remaining weight is distributed equally among the previous samples; this scheme is known as the homogeneously weighted moving average (HWMA) monitoring scheme. The HWMA scheme is by its nature a memory-type scheme used to effectively monitor small-to-moderate shifts (see for example the following articles on the HWMA-type monitoring schemes: Abbas (2018), Abbas et al (2020), Abid et al (2020a, b), Adegoke et al (2019a, b), Adeoti and Koleoso (2020), Dawod et al (2020), Nawaz and Han (2020), Raza et al (2020). Nawaz and Han (2020) studied the performance of the HWMA scheme using structured sampling techniques, that is, ranked set sampling (RSS), extreme RSS, median RSS and neoteric RSS.…”

Section: Introductionmentioning

confidence: 99%

“…The HWMA scheme is by its nature a memory-type scheme used to effectively monitor small-to-moderate shifts (see for example the following articles on the HWMA-type monitoring schemes: Abbas (2018), Abbas et al (2020), Abid et al (2020a, b), Adegoke et al (2019a, b), Adeoti and Koleoso (2020), Dawod et al (2020), Nawaz and Han (2020), Raza et al (2020). Nawaz and Han (2020) studied the performance of the HWMA scheme using structured sampling techniques, that is, ranked set sampling (RSS), extreme RSS, median RSS and neoteric RSS. To provide an efficient and unbiased estimate of the process mean, Adegoke et al (2019b) developed a HWMA scheme to monitor the process mean that uses the auxiliary variable in the form of a bivariate regression estimator.…”

Section: Introductionmentioning

confidence: 99%

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

Thanwane

Malela‐Majika

Castagliola

et al. 2020

Transactions of the Institute of Measurement and Control

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Monitoring schemes are typically designed under the assumption of perfect measurements. However, in real-life applications, data tend to be subjected to measurement errors, that is, a difference between the real quantities and the measured ones mostly exist even with highly sophisticated advanced measuring instruments. Thus, in this paper, the negative effect of measurement errors on the performance of the homogenously weighted moving average (HWMA) scheme is studied using the linear covariate error model for constant and linearly increasing variance. Monte Carlo simulations are used to evaluate the performance of the proposed HWMA scheme in terms of the run-length characteristics. It is observed that as the smoothing parameter increases, measurement errors have a higher negative effect on the performance of the HWMA [Formula: see text] scheme. More importantly, it is shown that the negative effect of measurement errors is reduced by using multiple measurements and/or by increasing the slope coefficient of the covariate error model. Moreover, the performance of the HWMA [Formula: see text] scheme is compared with the corresponding exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) [Formula: see text] schemes. An illustrative example is provided to help in implementing this monitoring scheme in a real-life situation.

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Monitoring the process location by using new ranked set sampling-based memory control charts

Cited by 33 publications

References 52 publications

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules

A mixed HWMA‐CUSUM mean chart with an application to manufacturing process

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

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Monitoring the process location by using new ranked set sampling-based memory control charts

Cited by 33 publications

References 52 publications

Generalized Hotelling T2 control chart based on bivariate ranked set techniques with runs rules

Generalized Hotelling T2 control chart based on bivariate ranked set techniques with runs rules

A mixed HWMA‐CUSUM mean chart with an application to manufacturing process

The effect of measurement errors on the performance of the homogenously weighted moving average X¯ monitoring scheme

Contact Info

Product

Resources

About

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules

Generalized Hotelling T² control chart based on bivariate ranked set techniques with runs rules