Improved control charts for fraction non-conforming based on hypergeometric distribution

Chukhrova, Nataliya; Johannssen, Arne

doi:10.1016/j.cie.2018.12.066

Cited by 21 publications

(15 citation statements)

References 42 publications

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“…The control chart is a vital tool in statistical process control (SPC), and it is often viewed as an effective process monitoring technique in industries to detect the presence of assignable causes. This can be shown through a variety of recent publications, such as those by Aldosari et al, Chukhrova and Johannssen, Huberts et al, and Tran et al, to name a few. In many real‐life applications, a simultaneous monitoring of at least two related quality variables is necessary.…”

Section: Introductionmentioning

confidence: 91%

“…, (2) weighted root mean square, and (3) maximum likelihood estimation. Among these three methods, the statistical efficiency of γ is found to be quite low.…”

mentioning

confidence: 99%

“…), Syn MCV (Khaw et al26 ), and standard MCV (Yeong et al 23 ) charts, in terms of the EATS 1 criterion, for p ∈ {2,3,4}, n 0 ∈ {7,10,15}, γ 0 ∈ {0.1,0.3,0.5}, t 1 = 0.1 (only applicable to the VP MCV and VSSI MCV charts), and (τ min , τ max ) =(1,2]. The VP MCV chart outperforms the VSSI MCV and standard MCV charts for detecting shifts in the interval (τ min , τ max ) =(1,2]. For example, when p = 4, n 0 = 15, t 1 = 0.1, and γ 0 = 0.5, EATS 1 = 18.52, 21.31, 21.02, and 31.09, for the VP MCV, VSSI MCV, Syn MCV, and standard MCV charts, respectively, where the VP MCV chart has the smallest EATS 1 value.…”

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confidence: 99%

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A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

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An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.

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Section: Introductionmentioning

confidence: 91%

“…, (2) weighted root mean square, and (3) maximum likelihood estimation. Among these three methods, the statistical efficiency of γ is found to be quite low.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

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“…As the production process becomes more complex and automated, the actual data present the multiple patterns coupling, which may be coupled by two or more basic modes. More details regarding the control chart patterns (CCPs) can be found, Figure 1 and Figure 2 show the six basic CCPs and four mixture CCPs, respectively [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Zhang

Wang

et al. 2020

IEEE Access

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Control charts are significant diagnostic tools to detect and identify the quality fluctuation of the complex industrial process. In the practical production process, attention is being paid to the monitoring of mixture control charts, which usually coupled by two or more basic control charts modes. This research is to present a hybrid pattern recognition method for mixture control charts. The proposed method mainly covers the feature fusion extraction (FFE) and kernel extreme learning machine (KELM) with modified grey wolf optimizer (MGWO). The FFE module applies the original data and their shape and statistical features as the features, then uses kernel entropy component analysis to reduce the feature dimension and extract valid features. One significant difficulty of KELM is to get suitable parameters like the penalty parameter and the kernel function parameter value. MGWO is established to the optimal tuning of KELM parameters, which improves the population initialization and nonlinear convergence factor of traditional grey wolf optimizer. The proposed methodology is promising to obtain a better classification recognition rate, less computational time and achieves more stable results in the pattern recognition problem of mixture control charts. INDEX TERMS Control chart patterns recognition, feature fusion extraction, kernel extreme learning machine, kernel entropy component analysis, modified grey wolf optimizer.

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“…Among the SPC tools, control chart which is a graphical display that distinguishes common and assignable causes of variation is most widely used in manufacturing industries and non‐manufacturing sectors. Some of the recent literature on control charts can be found in Faraz et al, Kim et al, Chukhrova and Johannssen, Adegoke et al, Haq and Khoo, Sabahno et al, and Zwetsloot and Ajadi . Walter A. Shewhart from Bell Telephone Laboratories developed the Shewhart control chart in the 1920s.…”

Section: Introductionmentioning

confidence: 99%

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

Haq

Khoo

2019

Quality & Reliability Eng

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Memory‐type multivariate charts have been widely recognized as a potentially powerful process monitoring tool because of their excellent speed in detecting small‐to‐moderate shifts in the mean vector of a multivariate normally distributed process, namely, the multivariate EWMA (MEWMA), double MEWMA, Crosier multivariate CUSUM (MCUSUM), and Pignatiello and Runger MCUSUM charts. These multivariate charts are based on the assumption that the covariance matrix is known in advance; but, it may not be known in practice. It is thus not possible to use these multivariate charts unless a large Phase I dataset is available from an in‐control process. In this paper, we propose multivariate charts with fixed and variable sampling intervals for the process mean vector when the covariance matrix is estimated from sample. Using the Monte Carlo simulation method, the run length characteristics of the multivariate charts are computed. It is shown that the in‐control and out‐of‐control run length performances of the proposed multivariate charts are robust to the changes in the process covariance matrix, while the existing multivariate charts are not. A real dataset is taken to explain the implementation of the proposed multivariate charts.

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Improved control charts for fraction non-conforming based on hypergeometric distribution

Cited by 21 publications

References 42 publications

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

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