An attribute control chart for multivariate Poisson distribution using multiple dependent state repetitive sampling

Aldosari, Mansour Sattam; Aslam, Muhammad; Rao, G. Srinivasa

doi:10.1002/qre.2426

Cited by 17 publications

(6 citation statements)

References 23 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%

“…Two separate one-sided standard MCV charts can be constructed as follows: (1) The downward chart consists of the lower control limit (LCL 0 ) for detecting the downward MCV shift, and (2) the upward chart consists of the upper control limit (UCL 0 ) for detecting the upward MCV shift. By setting the type I error probability of each of the charts as α 0 , the control limits are obtained as follows:…”

Section: Standard Multivariate CV Chartmentioning

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%

Section: Standard Multivariate CV Chartmentioning

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

View full text Add to dashboard Cite

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“…for non-zero eigenvalue. In other words, conducting PCA in feature space is equivalent to solving the eigenvalue problem from Equation (12). After solving the eigenvalue problem, eigenvector α 1 , α 2 , .…”

Section: Kernel Pcamentioning

confidence: 99%

“…Meanwhile, the latest development of MEWMA and MCUSUM charts covers an adaptive MEWMA chart [7], one-sided and two one-sided MEWMA charts [8], dual MCUSUM chart [9], Max MCUSUM for autocorrelated data [10], as well as the mixed multivariate memory control charts [11]. Other recent charts of the multiattribute charts consist of multiple dependent state repetitive sampling (MDSRS) [12] and fuzzy bivariate chart [13] for Poisson distribution, as well as the multinomial generalized likelihood ratio (MGLR) control chart for multinomial distribution [14].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics

et al. 2020

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The need for a control chart that can visualize and recognize the symmetric or asymmetric pattern of the monitoring process with more than one type of quality characteristic is a necessity in the era of Industry 4.0. In the past, the control charts were only developed to monitor one kind of quality characteristic. Several control charts were created to deal with this problem. However, there are some problems and drawbacks to the conventional mixed charts. In this study, another approach is used to monitor mixed quality characteristics by applying the Kernel Principal Component Analyisis (KPCA) method. Using the Hotelling’s T2 statistic, the kernel PCA mix chart is proposed to simultaneously monitor the variable and attribute quality characteristics. Due to its ability to estimate the asymmetric pattern of the mixed process, the kernel density estimation (KDE) used in the proposed chart has successfully estimated the control limits that produce ARL0 at about 370 for α=0.00273. Through several experiments based on the proportion of the attribute characteristics and kernel functions, the proposed chart demonstrates better performance in detecting outlier and shift in the process. When it is applied to monitor the synthetic data, the proposed chart can detect the shift accurately. Additionally, the proposed chart outperforms the performance of the conventional mixed chart based on PCA mix by producing lower false alarm with more accurate detection of out of control processes.

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Generalized multiple dependent state optimal control charts under ranked set sampling for monitoring process mean

Nawaz

Raza

Han

2020

Quality & Reliability Eng

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This paper proposes two optimal control charts by using the generalized multiple dependent state (GMDS) plan to monitor the process mean. These charts make advantageous use of the ranked set sampling schemes and integrate them with the GMDS plan to improve the shift detection ability of the Shewhart‐type control charts. The proposed control charts have two pairs of control limits, inner and outer, which enables them to consider the current and previous sample information while making a decision about the process state. The run‐length profiles of the proposed charts are obtained through extensive Monte Carlo simulations. The individual and overall mean shift detection abilities of the proposed charts are compared with various competing control charts by using out‐of‐control average run length ( ) and the average extra quadratic loss ( ) as performance metrics. The comparison establishes the complete dominance of the proposed charting strategy. In the end, the practical implementation of the proposed charts is demonstrated by using a real data set related to the cement manufacturing industry.

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An attribute control chart for multivariate Poisson distribution using multiple dependent state repetitive sampling

Cited by 17 publications

References 23 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

Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics

Generalized multiple dependent state optimal control charts under ranked set sampling for monitoring process mean

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