The Experts below are selected from a list of 135738 Experts worldwide ranked by ideXlab platform
Philippe Castagliola - One of the best experts on this subject based on the ideXlab platform.
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the double sampling s2 chart with estimated Process Variance
Communications in Statistics-theory and Methods, 2017Co-Authors: Philippe Castagliola, Pedro Carlos Oprime, Michael B C KhooAbstract:ABSTRACTThis paper proposes useful exact bounds for the parameters of the double sampling S2 chart with known Process Variance and it also investigates the properties of the double sampling S2 chart with estimated Process Variance, in terms of the average run length, the standard deviation of the run length and the average sample size, providing a numerical comparison with the known Process Variance case. It also provides guidelines to systematically design the double sampling S2 chart both with known and estimated Process Variance and proposes two optimal design procedures with estimated Process Variance, for (a) minimizing the out-of-control average run length and (b) minimizing the out-of-control average sample size.
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the double sampling s2 chart with estimated Process Variance
Communications in Statistics-theory and Methods, 2017Co-Authors: Philippe Castagliola, Pedro Carlos Oprime, Michael B C KhooAbstract:This paper proposes useful exact bounds for the parameters of the double sampling S2 chart with known Process Variance and it also investigates the properties of the double sampling S2 chart with e...
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the exact run length distribution and design of the s2 chart when the in control Variance is estimated
International Journal of Reliability Quality and Safety Engineering, 2009Co-Authors: Philippe Castagliola, Giovanni Celano, Gemai ChenAbstract:When monitoring the Process variability, it is a common practice that a Phase I data set is used to estimate the unknown in-control Process standard deviation σ0 or Variance to set up the control limits, then monitoring proceeds. Once the Process is considered to be in-control, the estimated control limits are assumed as fixed. This practice ignores the effect of estimating the unknown in-control Process Variance . In this paper, we derive the exact run length distribution of the S2 control chart when the in-control Process Variance is estimated and find that m = 200 or more Phase I samples are needed to neglect the effect of using estimated control limits. New control limits when m is small are also derived.
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a new cusum s2 control chart for monitoring the Process Variance
International Conference on Industrial Engineering and Systems Management, 2009Co-Authors: Philippe Castagliola, Giovanni Celano, S FicheraAbstract:Purpose – The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample Variance of samples from a normally distributed population.Design/methodology/approach – The proposed chart monitors a statistic computed as a logarithmic transformation of the sample Variance; the introduction of the sample Variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.Findings – A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S2 chart has been assessed through a comparison with an EWMA‐S2 control chart proposed in the literature to monitor the Process dispersion.Research limitations/implications – The paper only deals with uncorrelated normally distributed data.Practical implications – The obtained results show how the CUSUM‐S2 ch...
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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.
Michael B C Khoo - One of the best experts on this subject based on the ideXlab platform.
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new adaptive ewma control charts for monitoring univariate and multivariate coefficient of variation
Computers & Industrial Engineering, 2019Co-Authors: Michael B C KhooAbstract:Abstract The coefficient of variation (CV), a measure of relative variability, is an important quality control issue worthy of consideration in some manufacturing and service-oriented companies when the Process mean is not constant and/or the Process Variance is a function of the Process mean. In this paper, we propose two adaptive EWMA (AEWMA) charts for monitoring the infrequent changes in the CV and multivariate CV (MCV) when sampling from univariate and multivariate normally distributed Processes, named the AEWMA CV and AEWMA MCV charts, respectively. With extensive Monte Carlo simulations, the run length characteristics of the proposed control charts are computed. It is found that the AEWMA CV chart performs substantially and uniformly better than the existing optimal EWMA and CUSUM CV charts when detecting moderate-to-large shifts in the Process CV. Moreover, the AEWMA MCV chart also performs substantially and uniformly better than the existing Shewhart MCV chart. The proposed control charts are implemented on real datasets to support the theory.
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the double sampling s2 chart with estimated Process Variance
Communications in Statistics-theory and Methods, 2017Co-Authors: Philippe Castagliola, Pedro Carlos Oprime, Michael B C KhooAbstract:ABSTRACTThis paper proposes useful exact bounds for the parameters of the double sampling S2 chart with known Process Variance and it also investigates the properties of the double sampling S2 chart with estimated Process Variance, in terms of the average run length, the standard deviation of the run length and the average sample size, providing a numerical comparison with the known Process Variance case. It also provides guidelines to systematically design the double sampling S2 chart both with known and estimated Process Variance and proposes two optimal design procedures with estimated Process Variance, for (a) minimizing the out-of-control average run length and (b) minimizing the out-of-control average sample size.
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the double sampling s2 chart with estimated Process Variance
Communications in Statistics-theory and Methods, 2017Co-Authors: Philippe Castagliola, Pedro Carlos Oprime, Michael B C KhooAbstract:This paper proposes useful exact bounds for the parameters of the double sampling S2 chart with known Process Variance and it also investigates the properties of the double sampling S2 chart with e...
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monitoring Process mean and Variance with a single generally weighted moving average chart
Communications in Statistics-theory and Methods, 2012Co-Authors: Sin Yin Teh, Michael B C KhooAbstract:Two generally weighted moving average (GWMA) charts are usually used concurrently for a simultaneous monitoring of the Process mean and Process Variance. In this article, we propose a new GWMA chart, called the Max-GWMA chart, which uses a single statistic for a simultaneous monitoring of the Process mean and Variance. The statistic of the Max-GWMA chart is based on the maximum of the absolute values of two GWMA statistics, one for controlling the mean while the other the Variance. We show that the Max-GWMA chart outperforms the combined GWMA chart, in terms of the average run length (ARL), standard deviation of the run length (SDRL) and diagnostic abilities performances. The combined GWMA chart consists of two GWMA charts that are run concurrently, one for monitoring the mean and the other the Variance.
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a new bivariate control chart to monitor the multivariate Process mean and Variance simultaneously
Quality Engineering, 2004Co-Authors: Michael B C KhooAbstract:[This abstract is based on the author's abstract.]A control chart is proposed that simultaneously monitors both the Process mean and Process Variance of multivariate data. The identification of shifts of the Process mean or variability is also discussed..
Filippo Giuffrida - One of the best experts on this subject based on the ideXlab platform.
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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.
Yuan Yao - One of the best experts on this subject based on the ideXlab platform.
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Online Monitoring of Multivariate Processes Using Higher-Order Cumulants Analysis
Industrial & Engineering Chemistry Research, 2014Co-Authors: Youqing Wang, Jicong Fan, Yuan YaoAbstract:In this study, a novel approach is developed for online state monitoring based on higher-order cumulants analysis (HCA). This approach applies higher-order cumulants to monitor a multivariate Process, and while conventional approaches such as independent components analysis (ICA) uses Variance to monitor Process. Variance is lower-order statistics and is only sensitive to amplitude. In contrast, higher-order cumulants, the typical higher-order statistics, carry important information and are sensitive to both amplitude and phase, particularly for non-Gaussian distributions. The main idea of this novel approach is to monitor the cumulants of dominant independent components and residuals of the ICA model. Therefore, higher-order statistical information of multivariate Processes can be monitored online. Furthermore, a variable contribution analysis scheme is developed for HCA to diagnose faults. The proposed approach is applied to the Tennessee Eastman (TE) Process to exhibit its effectiveness. The results de...
Giovanni Celano - One of the best experts on this subject based on the ideXlab platform.
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the exact run length distribution and design of the s2 chart when the in control Variance is estimated
International Journal of Reliability Quality and Safety Engineering, 2009Co-Authors: Philippe Castagliola, Giovanni Celano, Gemai ChenAbstract:When monitoring the Process variability, it is a common practice that a Phase I data set is used to estimate the unknown in-control Process standard deviation σ0 or Variance to set up the control limits, then monitoring proceeds. Once the Process is considered to be in-control, the estimated control limits are assumed as fixed. This practice ignores the effect of estimating the unknown in-control Process Variance . In this paper, we derive the exact run length distribution of the S2 control chart when the in-control Process Variance is estimated and find that m = 200 or more Phase I samples are needed to neglect the effect of using estimated control limits. New control limits when m is small are also derived.
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a new cusum s2 control chart for monitoring the Process Variance
International Conference on Industrial Engineering and Systems Management, 2009Co-Authors: Philippe Castagliola, Giovanni Celano, S FicheraAbstract:Purpose – The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample Variance of samples from a normally distributed population.Design/methodology/approach – The proposed chart monitors a statistic computed as a logarithmic transformation of the sample Variance; the introduction of the sample Variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.Findings – A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S2 chart has been assessed through a comparison with an EWMA‐S2 control chart proposed in the literature to monitor the Process dispersion.Research limitations/implications – The paper only deals with uncorrelated normally distributed data.Practical implications – The obtained results show how the CUSUM‐S2 ch...
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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.