The Experts below are selected from a list of 93219 Experts worldwide ranked by ideXlab platform
Giovanni Celano - One of the best experts on this subject based on the ideXlab platform.
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a variable Sampling Interval shewhart control chart for monitoring the coefficient of variation in short production runs
International Journal of Production Research, 2017Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract:Monitoring the coefficient of variation (CV) allows process monitoring to be performed when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few research papers have investigated the monitoring of the CV in a short production run context. This paper investigates the design and implementation of a Variable Sampling Interval Shewhart control chart to monitor the coefficient of variation in a short production run context. Formulas for the truncated average time to signal are derived and a performance comparison is carried out with a Fixed Sampling Rate Shewhart chart monitoring the CV. An example illustrates the use of this chart on real industrial data.
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monitoring the coefficient of variation using a variable Sampling Interval control chart
Quality and Reliability Engineering International, 2013Co-Authors: Philippe Castagliola, Hassen Taleb, Giovanni Celano, Ali Achouri, Stelios PsarakisAbstract:The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. Few papers have proposed control charts that monitor this normalized measure of dispersion. In this paper, an adaptive Shewhart control chart implementing a variable Sampling Interval (VSI) strategy is proposed to monitor the CV. Tables are provided for the statistical properties of the VSI CV chart, and a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV. An example illustrates the use of these charts on real data gathered from a casting process. Copyright © 2012 John Wiley & Sons, Ltd.
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a variable Sampling Interval s2 ewma control chart for monitoring the process variance
International Journal of Technology Management, 2007Co-Authors: Philippe Castagliola, Giovanni Celano, S Fichera, Filippo GiuffridaAbstract:This paper proposes a Variable Sampling Interval version of the Fixed Sampling Interval S2-EWMA control chart developed by Castagliola (2004) and dedicated to the monitoring of the sample variance of a process. In this paper, we explain how the various parameters of this VSI S2-EWMA control chart can be computed and how the use of the VSI feature significantly improves the statistical efficiency of FSI S2-EWMA chart, thus representing an effective tool in the detection of process out-of-control conditions. An optimal design strategy based on the Average Time to Signal (ATS) is presented and a comparison with the FSI procedure is performed.
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statistical design of variable sample size and Sampling Interval bar x control charts with run rulescontrol charts with run rules
The International Journal of Advanced Manufacturing Technology, 2006Co-Authors: Giovanni Celano, Antonio Costa, Sergio FicheraAbstract:In this paper, improved Shewhart control charts based on hybrid adaptive and run rule schemes are introduced to enhance the statistical performances of the traditional static scheme, designed with consideration given to the fixed values of sample size, the width of the control limits and the Sampling frequency. The proposed hybrid adaptive schemes consider both variable Sampling Interval and variable sample size combined with run rules. The objective of this research is to develop a statistical comparison between adaptive schemes, charts with run rules and hybrid adaptive schemes with run rules to help decision-makers in the selection of the best performing chart for an expected value of shift in the mean of a controlled parameter. An extensive set of numerical results is presented to test the effectiveness of the proposed models in detecting small and moderate shifts in the process mean. The optimal statistical designs of the charts are obtained through a heuristic algorithm, properly modified to cope with the problem.
Michael B C Khoo - One of the best experts on this subject based on the ideXlab platform.
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a variable sample size and Sampling Interval control chart for monitoring the process mean using auxiliary information
Quality Technology and Quantitative Management, 2019Co-Authors: Sajal Saha, Ming Ha Lee, Michael B C Khoo, Abdul HaqAbstract:A variable sample size and Sampling Interval (VSSI) control chart using the auxiliary information (VSSI AI) is proposed for efficiently monitoring the process mean. The control chart’s statistics, ...
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a variable sample size and Sampling Interval control chart for monitoring the process mean using auxiliary information
Quality Technology and Quantitative Management, 2019Co-Authors: Sajal Saha, Ming Ha Lee, Michael B C Khoo, Abdul HaqAbstract:AbstractA variable sample size and Sampling Interval (VSSI) control chart using the auxiliary information (VSSI AI) is proposed for efficiently monitoring the process mean. The control chart’s statistics, optimal design and implementation are discussed. The average time to signal (ATS) and expected ATS (EATS) criteria are adopted to evaluate the performance of the VSSI AI chart. The ATS and EATS performances of the VSSI AI chart, exponentially weighted moving average (EWMA) chart, EWMA chart with AI (EWMA AI) and the synthetic chart with AI (Syn AI) are compared and explored for common patterns. The proposed VSSI AI chart shows a better performance than the EWMA, EWMA AI and Syn AI charts, in terms of the ATS and EATS criteria. An example is also presented to illustrate the implementation of the VSSI AI chart.
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double Sampling s control chart with variable sample size and variable Sampling Interval
Communications in Statistics - Simulation and Computation, 2018Co-Authors: Ming Ha Lee, Michael B C KhooAbstract:AbstractThis study presents a control chart for monitoring shifts in the covariance matrix of a multivariate normally distributed process. This chart combines the double Sampling, variable sample size and variable Sampling Interval features, and is called the DSVSSI |S| chart. A Markov chain approach is developed to design the DSVSSI |S| chart, by minimizing the average time to signal (ATS), for a specified shift size in the covariance matrix. The DSVSSI |S| chart has a better ATS performance compared to other existing charts. An example is given to illustrate the operation of the DSVSSI |S| chart.
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monitoring the coefficient of variation using a variable Sampling Interval ewma chart
Journal of Quality Technology, 2017Co-Authors: Wai Chung Yeong, Michael B C Khoo, L K Tham, Wei Lin Teoh, M A RahimAbstract:In recent years, the coefficient of variation (CV) chart is receiving increasing attention in quality control. A number of studies demonstrated that adaptive charts could detect process shifts faster than traditional charts. This paper proposes an EWMA chart with variable Sampling Interval (VSI) to monitor the CV. Formulas for computing the performance measures of the VSI EWMA-γ2 chart are derived using Markov chain, where γ2 denotes the CV squared. Comparative studies show that the VSI EWMA-γ2 chart significantly outperforms other competing charts. An example using real manufacturing data shows that the VSI EWMA-γ2 chart performs well in applications.
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monitoring the coefficient of variation using a variable sample size and Sampling Interval control chart
Communications in Statistics - Simulation and Computation, 2017Co-Authors: Khai Wah Khaw, Wai Chung Yeong, Michael B C Khoo, Zhang WuAbstract:ABSTRACTThis article proposes a CV chart by using the variable sample size and Sampling Interval (VSSI) feature to improve the performance of the basic CV chart, for detecting small and moderate shifts in the CV. The proposed VSSI CV chart is designed by allowing the sample size and the Sampling Interval to vary. The VSSI CV chart's statistical performance is measured by using the average time to signal (ATS) and expected average time to signal (EATS) criteria and is compared with that of existing CV charts. The Markov chain approach is employed in the design of the chart.
Ming Ha Lee - One of the best experts on this subject based on the ideXlab platform.
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a variable sample size and Sampling Interval control chart for monitoring the process mean using auxiliary information
Quality Technology and Quantitative Management, 2019Co-Authors: Sajal Saha, Ming Ha Lee, Michael B C Khoo, Abdul HaqAbstract:A variable sample size and Sampling Interval (VSSI) control chart using the auxiliary information (VSSI AI) is proposed for efficiently monitoring the process mean. The control chart’s statistics, ...
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a variable sample size and Sampling Interval control chart for monitoring the process mean using auxiliary information
Quality Technology and Quantitative Management, 2019Co-Authors: Sajal Saha, Ming Ha Lee, Michael B C Khoo, Abdul HaqAbstract:AbstractA variable sample size and Sampling Interval (VSSI) control chart using the auxiliary information (VSSI AI) is proposed for efficiently monitoring the process mean. The control chart’s statistics, optimal design and implementation are discussed. The average time to signal (ATS) and expected ATS (EATS) criteria are adopted to evaluate the performance of the VSSI AI chart. The ATS and EATS performances of the VSSI AI chart, exponentially weighted moving average (EWMA) chart, EWMA chart with AI (EWMA AI) and the synthetic chart with AI (Syn AI) are compared and explored for common patterns. The proposed VSSI AI chart shows a better performance than the EWMA, EWMA AI and Syn AI charts, in terms of the ATS and EATS criteria. An example is also presented to illustrate the implementation of the VSSI AI chart.
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double Sampling s control chart with variable sample size and variable Sampling Interval
Communications in Statistics - Simulation and Computation, 2018Co-Authors: Ming Ha Lee, Michael B C KhooAbstract:AbstractThis study presents a control chart for monitoring shifts in the covariance matrix of a multivariate normally distributed process. This chart combines the double Sampling, variable sample size and variable Sampling Interval features, and is called the DSVSSI |S| chart. A Markov chain approach is developed to design the DSVSSI |S| chart, by minimizing the average time to signal (ATS), for a specified shift size in the covariance matrix. The DSVSSI |S| chart has a better ATS performance compared to other existing charts. An example is given to illustrate the operation of the DSVSSI |S| chart.
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an improved switching rule in variable Sampling Interval hotelling s t2 control chart
Industrial Engineering and Engineering Management, 2015Co-Authors: Yiing Chee Tan, Ming Ha Lee, Winnie Wei Wan LamAbstract:In recent years, variable Sampling Interval (VSI) control charts have been shown to be more efficient than the corresponding fixed Sampling Interval control charts in detecting process shifts. However, the excessive number of switches between the different Sampling Intervals is undesirable in the application of the VSI control charts. This paper examines an improved switching rule based on the positions of the past and current samples for the VSI Hotelling's T2 control chart. The performance of the VSI Hotelling's T2 control chart with the improved switching rule is compared with the corresponding VSI Hotelling's T2 control chart without the improved switching rule.
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a variable Sampling Interval synthetic xbar chart for the process mean
PLOS ONE, 2015Co-Authors: Lei Yong Lee, Michael B C Khoo, Sin Yin Teh, Ming Ha LeeAbstract:The usual practice of using a control chart to monitor a process is to take samples from the process with fixed Sampling Interval (FSI). In this paper, a synthetic X¯ control chart with the variable Sampling Interval (VSI) feature is proposed for monitoring changes in the process mean. The VSI synthetic X¯ chart integrates the VSI X¯ chart and the VSI conforming run length (CRL) chart. The proposed VSI synthetic X¯ chart is evaluated using the average time to signal (ATS) criterion. The optimal charting parameters of the proposed chart are obtained by minimizing the out-of-control ATS for a desired shift. Comparisons between the VSI synthetic X¯ chart and the existing X¯, synthetic X¯, VSI X¯ and EWMA X¯ charts, in terms of ATS, are made. The ATS results show that the VSI synthetic X¯ chart outperforms the other X¯ type charts for detecting moderate and large shifts. An illustrative example is also presented to explain the application of the VSI synthetic X¯ chart.
Philippe Castagliola - One of the best experts on this subject based on the ideXlab platform.
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design of a variable Sampling Interval ewma median control chart
International Journal of Reliability Quality and Safety Engineering, 2019Co-Authors: Kim Phuc Tran, Philippe Castagliola, Thi Hien Nguyen, Anne CuzolAbstract:In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a...
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a variable Sampling Interval shewhart control chart for monitoring the coefficient of variation in short production runs
International Journal of Production Research, 2017Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract:Monitoring the coefficient of variation (CV) allows process monitoring to be performed when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few research papers have investigated the monitoring of the CV in a short production run context. This paper investigates the design and implementation of a Variable Sampling Interval Shewhart control chart to monitor the coefficient of variation in a short production run context. Formulas for the truncated average time to signal are derived and a performance comparison is carried out with a Fixed Sampling Rate Shewhart chart monitoring the CV. An example illustrates the use of this chart on real industrial data.
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the variable Sampling Interval run sum x control chart
Computers & Industrial Engineering, 2015Co-Authors: Xinying Chew, Sin Yin Teh, Michael B C Khoo, Philippe CastagliolaAbstract:Sampling Intervals for VSI run sum X ? chart are varied according to process quality.Optimization programs to minimize ATS 1 ( ? opt ) and AATS 1 ( ? opt ) for the VSI run sum X ? chart are developed.The zero state and steady state VSI run sum X ? chart's performances are evaluated.The VSI run sum X ? chart performs well compared with other competing charts.An example of application explains the construction of the VSI run sum X ? chart. Traditional control charts for process monitoring are based on taking samples from the process at fixed length Sampling Intervals. More recently, research works focused on the use of variable Sampling Intervals (VSIs), where the lengths of the Sampling Intervals are varied according to the process quality. A short Sampling Interval is considered when the process quality indicates a possible out-of-control situation while a long Sampling Interval is considered, otherwise. In this paper, the VSI run sum (RS) X ? chart is proposed with its optimal scores and parameters determined using an optimization technique to minimize the out-of-control average time to signal (ATS) or the adjusted average time to signal (AATS). A Markov-chain method is used to evaluate both the ATS and AATS of the proposed chart, for the zero and steady state cases, respectively. Results show that the VSI RS X ? chart is considerably more efficient than the basic RS X ? chart. The VSI RS X ? chart performs generally well compared with other competing charts, such as the standard X ? , synthetic X ? , exponentially weighted moving average (EWMA) X ? , VSI X ? and VSI EWMA X ? charts. The sensitivity of the VSI RS X ? chart can be enhanced further by adding more scoring regions or a head-start feature. An illustrative example is presented to explain the implementation of the proposed VSI RS X ? chart.
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monitoring the coefficient of variation using a variable Sampling Interval control chart
Quality and Reliability Engineering International, 2013Co-Authors: Philippe Castagliola, Hassen Taleb, Giovanni Celano, Ali Achouri, Stelios PsarakisAbstract:The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. Few papers have proposed control charts that monitor this normalized measure of dispersion. In this paper, an adaptive Shewhart control chart implementing a variable Sampling Interval (VSI) strategy is proposed to monitor the CV. Tables are provided for the statistical properties of the VSI CV chart, and a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV. An example illustrates the use of these charts on real data gathered from a casting process. Copyright © 2012 John Wiley & Sons, Ltd.
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the variable Sampling Interval x chart with estimated parameters
Quality and Reliability Engineering International, 2012Co-Authors: Philippe Castagliola, Ying Zhang, Michael B C KhooAbstract:The VSI chart has been investigated by many researchers under the assumption of known process parameters. However, in practice, these parameters are usually unknown and it is necessary to estimate them from the past data. In this paper, we evaluate and compare the performance of the VSI chart in terms of its average time to signal in the case where the process parameters are known and in the case where these parameters are estimated. We also provide new chart constants taking into account the number of phase I samples. Copyright © 2011 John Wiley & Sons, Ltd.
K.t. Lee - One of the best experts on this subject based on the ideXlab platform.
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variable Sampling Interval x control charts with an improved switching rule
International Journal of Production Economics, 2002Co-Authors: Do Sun Bai, K.t. LeeAbstract:Abstract This paper proposes variable Sampling Interval X control charts with an improved switching rule which uses a long Sampling Interval if l2(⩾2) consecutive sample means fall close to centerline and short Interval otherwise. A Markov chain approach is used to derive the formulas for evaluating the average time to signal and the average number of switches to signal. Comparisons between the proposed and existing variable Sampling Interval X control charts indicate that the proposed charts considerably reduce the average number of switches between short and long Sampling Intervals, while they are comparable with respect to the average time to signal.
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an economic design of variable Sampling Interval x control charts
International Journal of Production Economics, 1998Co-Authors: Do Sun Bai, K.t. LeeAbstract:This paper considers an economic design of variable Sampling Interval X control charts in which the Sampling Interval between two successive Sampling points is varied based on the value of the preceding sample mean. A cost model is constructed which involves the cost of false alarms, the cost of detecting and eliminating an assignable cause, the cost associated with production in out-of-control state and the cost of Sampling and testing. A method of finding optimal values of sample size, Sampling Interval lengths, control limits and threshold limits to select one of the Sampling Interval lengths is presented. Variable and fixed Sampling Interval X control charts are compared with respect to the expected cost per unit time.
