An overview of synthetic‐type control charts: Techniques and methodology

Rakitzis, Athanasios C.; Chakraborti, S.; Shongwe, Sandile Charles; Graham, Marien Alet; Khoo, Michael B. C.

doi:10.1002/qre.2491

Cited by 28 publications

(35 citation statements)

References 96 publications

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“…Moreover, similar to Noorossana et al (2015), the proposed VSSI design can be combined with the improved double sampling design proposed in Malela-Majika et al (2019) to monitor the mean under the combined effect of autocorrelation and measurement errors. To further improve the detection ability of the process mean under the combined effect of autocorrelation and measurement errors, a number of adaptive synthetic schemes (see Rakitzis et al, 2019) based on the proposed VSSI design can be constructed. Also, when the underlying distribution departs from normality, the properties of the proposed scheme need to be re-investigated.…”

Section: Resultsmentioning

confidence: 99%

Combined effect of autocorrelation and measurement errors on the adaptive X¯ monitoring schemes

Shongwe

Malela‐Majika

2020

Transactions of the Institute of Measurement and Control

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For independent and identically distributed observations, and those with measurement errors only, the adaptive designs (i.e. variable sampling sizes (VSS), variable sampling intervals (VSI) and the latter two combined to form VSSI) have been thoroughly discussed. However, no research exists for processes under the combined effect of autocorrelation and measurement errors; thus, such adaptive Shewhart [Formula: see text] schemes are proposed. The Markov chain approach for adaptive designs are used to evaluate the run-length distribution properties with two special sampling strategies (i.e. s-skip and multiple measurements) incorporated to reduce the combined negative effect of autocorrelation and measurement inaccuracy. Using numerous run-length metrics, it is shown that the combination of the two sampling strategies with the VSSI design reduces this negative effect considerably and improves the detection ability of the [Formula: see text] scheme by a significant margin as compared with using the fixed sample size and sampling interval (FSSI), VSS and VSI designs. Autocorrelation level has a higher negative effect as compared with the measurement inaccuracy level. For high levels of autocorrelation ([Formula: see text]0.8), the s-skip strategy has little influence in reducing the negative effect; but the VSSI design maintains better performance than the other designs. Finally, a real-life example is used to illustrate its implementation.

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Section: Resultsmentioning

confidence: 99%

Combined effect of autocorrelation and measurement errors on the adaptive X¯ monitoring schemes

Shongwe

Malela‐Majika

2020

Transactions of the Institute of Measurement and Control

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“…A number of authors have shown that if a monitoring scheme is designed based on one specific shift size δ , it would perform poorly when it is considerably different from the assumed one; see Reynolds and Lou and Ryu et al This makes the ARL inefficient in assessing the overall performance of a monitoring scheme. Thus, in addition to specific shift measures, ie, Equations and , researchers are encouraged to use some overall performance metric, like the expected ARL ( EARL ) because users tend not to know beforehand what exact shift value(s) is targeted—see, for example, Machado and Costa, Huh, (chapter 4) Tran et al, Malela‐Majika and Rapoo, You, Shongwe and Graham, and Rakitzis et al The EARL measures the performance of a monitoring scheme over a range of shift values, ie, δ min to δ max —which are the lower and the upper bound of δ , respectively. Note that the shifts within the interval [ δ min , δ max ] usually occur according to a probability density function (p.d.f.)…”

Section: Run‐length Properties Of the Rr W‐of‐w S‐and‐m Monitoring Schemementioning

confidence: 99%

On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme

Shongwe

Malela‐Majika

Castagliola

2020

Quality & Reliability Eng

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Runs‐rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs‐rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w‐of‐w runs‐rules to improve the performance of the Shewhart trueX¯ scheme using an additive model with a constant variance and a first‐order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non‐neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed‐form expressions for the zero‐ and steady‐state run‐length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart trueX¯ scheme by a significant margin. A real‐life example is used to illustrate the practical implementation of the proposed scheme.

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“…It is observed that, in zero state, synthetic control chart performs better than X chart. Rakitzis et al 14 developed synthetic-type control chart. They proposed two types of synthetic control charts, namely, simple synthetic chart and improved synthetic chart.…”

Section: Synthetic Control Chartmentioning

confidence: 99%

‘Alternated Charting Statistic Modified Group Runs’ (ACS‐MGR) control chart for monitoring mean vectors of bivariate and trivariate processes

Gadre

Venegurkar

2020

Quality & Reliability Eng

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In this article, we propose "Alternated Charting Statistic-Modified Group Runs" (ACS-MGR) control chart for monitoring mean vectors of bivariate and trivariate processes, as an improvement over the ACS chart. This chart is obtained by combining ACS chart with the MGR chart, and it is verified that ACS-MGR chart significantly reduces "Average Time to Signal" (ATS) as compared with ACS chart, when the process has gone out of control. Steady state performance of the chart is also studied by comparing with steady state performance of the ACS-Syn chart and ACS-GR chart. K E Y W O R D S average time to signal, group runs control chart, modified group runs control chart, synthetic control chart, zero state and steady state

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An overview of synthetic‐type control charts: Techniques and methodology

Cited by 28 publications

References 96 publications

Combined effect of autocorrelation and measurement errors on the adaptive X¯ monitoring schemes

Combined effect of autocorrelation and measurement errors on the adaptive X¯ monitoring schemes

On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme

‘Alternated Charting Statistic Modified Group Runs’ (ACS‐MGR) control chart for monitoring mean vectors of bivariate and trivariate processes

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