Performance of maximum EWMA control chart in the presence of measurement error using auxiliary information

Noor‐ul‐Amin, Muhammad; Javaid, Amjad; Hanif, Muhammad; Doğu, Eralp

doi:10.1080/03610918.2020.1772301

Cited by 25 publications

(15 citation statements)

References 52 publications

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“…Therefore, these designs are preferred to be implemented for the industries that are sensitive toward small shifts, and the magnitude of these shifts can counter serious production issues. Now, all these charts are known as the classical control charts despite having some new modifications, as they can deal only specified given shifts like Abbasi, 4 Abujiya et al, 5 Hussain et al, 6 Noor-ul-Amin et al, 7 and Noor-ul-Amin. 8 If productions must pass through larger shifts, then these developments are not sufficient to efficiently detect moderatelarge shifts, and these cannot be taken as efficient to implement in all types of expected productions' shifts within a given range, as it does not seem practical to get the assessment of the production shifts in advance or based on some phase I sampling knowledge.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Function‐based adaptive exponentially weighted moving average dispersion control chart

Arshad

Noor‐ul‐Amin

Hanif

2021

Quality & Reliability Eng

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In this study, a new process dispersion monitoring control chart is proposed based on a function-based adaptation to select the smoothing constant value, named as a function-based adaptive exponentially weighted moving average (EWMA) dispersion control chart. It is suggested to track shift ranges first expected in process dispersion by opting for smoothing constant computation with the help of a function. The shift magnitude assessment is made by an unbiased estimator that determines the smoothing constant value through the proposed function. The enhanced efficiency of the proposed chart can be assessed in terms of smaller run-length profiles, which are determined through Monte Carlo simulations. The proposed chart is compared with the existing adaptive EWMA dispersion chart, and it turned out to perform substantially efficiently in detecting all kinds of decreasing and increasing process dispersion shift magnitudes.Moreover, a real-life dataset application is explained in the example section to elaborate on the ease of implementation in the real-life scenario.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Function‐based adaptive exponentially weighted moving average dispersion control chart

Arshad

Noor‐ul‐Amin

Hanif

2021

Quality & Reliability Eng

Self Cite

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“…Note that the effect of measurement errors on the performance of HWMA scheme to monitor the process mean is discussed in Thanwane et al 18,19 for Cases K and U, respectively. For some early discussions on measurement errors in SPM context, see Linna and Woodall, 27 Maravelakis et al, 28 and Maravelakis 29 ; however, for some recent contributions, see Riaz et al, 30 Sabanho et al, 31,32 Zaidi et al, 33,34 Nguyen et al, 35 and Noor-ul-Amin et al 36 The objective of this paper is to incorporate FIR features in the time-varying control limits of the HWMA scheme to monitor the mean of processes with and without measurement errors so that its sensitivity in detecting start-up problems can be improved. In the review papers by Jensen et al, 37 Psarakis et al, 38 and Does et al, 4 it is stated that the estimation of the process parameters significantly degrades the performance of a monitoring scheme; thus, the investigation of the effect of parameter estimation on the performance of the HWMĀscheme for both a constant and linearly increasing measurement system error variances is conducted.…”

Section: Introductionmentioning

confidence: 99%

The use of fast initial response features on the homogeneously weighted moving average chart with estimated parameters under the effect of measurement errors

Thanwane

Malela‐Majika

Aslam

et al. 2021

Quality & Reliability Eng

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Fast initial response (FIR) features are generally used to improve the sensitivity of memory-type control charts by shrinking time-varying control limits in the earlier stage of the monitoring regime. This paper incorporates FIR features to increase the sensitivity of the homogeneously weighted moving average (HWMA) monitoring schemes with and without measurement errors under constant as well as linearly increasing variance scenarios. The robustness and the performance of the HWMA monitoring schemes are investigated in terms of numerous run-length properties assuming that the underlying process parameters are known and unknown. It is found that the FIR features improves the performance of the HWMA monitoring scheme as compared to the standard no FIR feature HWMA scheme, and at the same time, it is observed that the simultaneous use of a recently proposed FIR feature and multiple measurements significantly reduces the negative effect of measurement errors. An illustrative example on the volume of milk in bottles is used to demonstrate a real-life application.

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“…Next, Maravelakis et al (2004) and Maravelakis (2012) investigated the effect of measurement errors on the EWMA and CUSUM schemes, respectively; with the effect of a two-component measurement error on the EWMA scheme investigated in Abbasi (2016). For some recent discussions on measurement errors published after the review paper of Maleki et al (2017), see for instance Asif et al (2020), Cheng and Wang (2018), Nguyen et al (2019), Noor-ul-Amin et al (2020), Riaz et al (2019), Sabahno et al (2019, 2020), Salmasnia et al (2018), Shongwe et al (2020a, b, c), Tang et al (2019), Tran et al (2019a, b, c, 2020), Yeong et al (2017), Zaidi et al (2019, 2020).…”

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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Performance of maximum EWMA control chart in the presence of measurement error using auxiliary information

Cited by 25 publications

References 52 publications

Function‐based adaptive exponentially weighted moving average dispersion control chart

Function‐based adaptive exponentially weighted moving average dispersion control chart

The use of fast initial response features on the homogeneously weighted moving average chart with estimated parameters under the effect of measurement errors

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

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