Remedial Measures to Lessen the Effect of Imprecise Measurement with Linearly Increasing Variance on the Performance of the MAX-EWMAMS Scheme

Salmasnia, Ali; Maleki, Mohammad Reza; Niaki, Seyed Taghi Akhavan

doi:10.1007/s13369-017-2896-1

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…The effect of measurement errors on simultaneous control charts for monitoring the mean and variability has been considered by Ghashghaei et al, Khati Dizabadi et al, and Salmasnia et al for univariate charts and by Amiri et al and Maleki et al for multivariate charts. In addition, Maleki et al performed a literature review about the measurement errors in statistical process monitoring (SPM) based on 60 papers published in this domain.…”

Section: Introductionmentioning

confidence: 99%

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Sabahno

Castagliola

Amiri

2020

Quality & Reliability Eng

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Evaluating the effect of measurement errors on either adaptive or simultaneous control charts has been a topic of interest for the researchers in the recent years. Nevertheless, the effect of measurement errors on both adaptive and simultaneous monitoring control charts has not been considered yet. In this paper, through extensive numerical studies, we evaluate the effect of measurement errors on an adaptive (variable parameters) simultaneous multivariate control chart for the mean vector and the variancecovariance matrix of p quality characteristics assumed to follow a multivariate normal distribution. In order to do so, (a) we use eight performance measures computed using a Markov chain model, (b) we consider the effects of multiple measurements as well as the error model's parameters, and (c) we also consider the overall performance of this adaptive simultaneous chart including the chart parameters values optimization, which have never been considered so far for this scheme. At last, a real case is presented in order to illustrate the proposed scheme. K E Y W O R D S

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Section: Introductionmentioning

confidence: 99%

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Sabahno

Castagliola

Amiri

2020

Quality & Reliability Eng

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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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“…In the review paper on measurement errors, Maleki et al (2017) stated that wherever there is a human involvement, an exact measurement is a rare phenomenon in any manufacturing and service environment; hence, a difference between the real quantities and the measured ones will always exist even with highly sophisticated advanced measuring instruments. Moreover, Maleki et al (2017) discussed the remedial approaches used to reduce the negative effect of measurement errors; see also Salmasnia et al (2018), Tang et al (2019) and Tran et al (2019). For the Shewhart X ¯ scheme, the effect of measurement errors on the VSI, VSS and VSSI designs were recently studied in Hu et al (2016a, 2016b) and Sabahno and Amiri (2017), respectively.…”

Section: Introductionmentioning

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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Remedial Measures to Lessen the Effect of Imprecise Measurement with Linearly Increasing Variance on the Performance of the MAX-EWMAMS Scheme

Cited by 14 publications

References 21 publications

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

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

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

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