The Experts below are selected from a list of 475182 Experts worldwide ranked by ideXlab platform
Hiromu Ohno - One of the best experts on this subject based on the ideXlab platform.
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Statistical Process monitoring based on dissimilarity of Process data
Aiche Journal, 2002Co-Authors: Manabu Kano, Shinji Hasebe, Iori Hashimoto, Hiromu OhnoAbstract:Multivariate Statistical Process control (MSPC) has been widely used for monitoring chemical Processes with highly correlated variables. In this work, a novel Statistical Process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of Process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 Process and the Tennessee Eastman Process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected.
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a new multivariate Statistical Process monitoring method using principal component analysis
Computers & Chemical Engineering, 2001Co-Authors: Manabu Kano, Shinji Hasebe, Iori Hashimoto, Hiromu OhnoAbstract:Abstract Principal component analysis (PCA) has been used successfully as a multivariate Statistical Process control (MSPC) tool for detecting faults in Processes with highly correlated variables. In the present work, a novel Statistical Process monitoring method is proposed for further improvement of monitoring performance. It is termed ‘moving principal component analysis’ (MPCA) because PCA is applied on-line by moving the time-window. In MPCA, changes in the direction of each principal component or changes in the subspace spanned by several principal components are monitored. In other words, changes in the correlation structure of Process variables, instead of changes in the scores of predefined principal components, are monitored by using MPCA. The monitoring performance of the proposed method and that of the conventional MSPC method are compared with application to simulated data obtained from a simple 2×2 Process and the Tennessee Eastman Process. The results clearly show that the monitoring performance of MPCA is considerably better than that of the conventional MSPC method and that dynamic monitoring is superior to static monitoring.
Peihua Qiu - One of the best experts on this subject based on the ideXlab platform.
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some perspectives on nonparametric Statistical Process control
Journal of Quality Technology, 2018Co-Authors: Peihua QiuAbstract:Statistical Process control (SPC) charts play a central role in quality control and management. Many conventional SPC charts are designed under the assumption that the related Process distribution is normal. In practice, the normality assumption is often invalid. In such cases, some articles show that certain conventional SPC charts are robust and can still be used as long as their parameters are properly chosen. Some other articles argue that results from such conventional SPC charts would not be reliable and that nonparametric SPC charts should be considered instead. In recent years, many nonparametric SPC charts have been proposed. Most of them are based on the ranking information in Process observations collected at different time points. Some of them are based on data categorization and categorical data analysis. In this article, we give some perspectives on issues related to the robustness of conventional SPC charts and to the strengths and limitations of various nonparametric SPC charts.
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Statistical Process Control Using a Dynamic Sampling Scheme
Technometrics, 2014Co-Authors: Peihua QiuAbstract:This article considers Statistical Process control (SPC) of univariate Processes, and tries to make two contributions to the univariate SPC problem. First, we propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a Process distributional shift at each observation time point, provided by the p-value of the conventional cumulative sum (CUSUM) charting statistic. For convenience of the design and implementation, the variable sampling scheme is described by a parametric function in the flexible Box–Cox transformation family. Second, the resulting CUSUM chart using the variable sampling scheme is combined with an adaptive estimation procedure for determining its reference value, to effectively protect against a range of unknown shifts. Numerical studies show that it performs well in various cases. A real data example from a chemical Process illustrates the application and implementation of our proposed method. This article has supplementary materials online.
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introduction to Statistical Process control
2013Co-Authors: Peihua QiuAbstract:Introduction Quality and the Early History of Quality Improvement Quality Management Statistical Process Control Organization of the Book Basic Statistical Concepts and Methods Introduction Population and Population Distribution Important Continuous Distributions Important Discrete Distributions Data and Data Description Tabular and Graphical Methods for Describing Data Parametric Statistical Inferences Nonparametric Statistical Inferences Univariate Shewhart Charts and Process Capability Introduction Shewhart Charts for Numerical Variables Shewhart Charts for Categorical Variables Process Capability Analysis Some Discussions Univariate CUSUM Charts Introduction Monitoring the Mean of a Normal Process Monitoring the Variance of a Normal Process CUSUM Charts for Distributions in Exponential Family Self-Starting and Adaptive CUSUM Charts Some Theory for Computing ARL Values Some Discussions Univariate EWMA Charts Introduction Monitoring the Mean of a Normal Process Monitoring the Variance of a Normal Process Self-Starting and Adaptive EWMA Charts Some Discussions Univariate Control Charts by Change-Point Detection Introduction Univariate Change-Point Detection Control Charts by Change-Point Detection Some Discussions Multivariate Statistical Process Control Introduction Multivariate Shewhart Charts Multivariate CUSUM Charts Multivariate EWMA Charts Multivariate Control Charts by Change-Point Detection Multivariate Control Charts by LASSO Some Discussions Univariate Nonparametric Process Control Introduction Rank-Based Nonparametric Control Charts Nonparametric SPC by Categorical Data Analysis Some Discussions Multivariate Nonparametric Process Control Introduction Rank-Based Multivariate Nonparametric Control Charts Multivariate Nonparametric SPC by Log-Linear Modeling Some Discussions Profile Monitoring Introduction Parametric Profile Monitoring Nonparametric Profile Monitoring Some Discussions Appendix A: R Functions for SPC Appendix B: Datasets Used in the Book Bibliography Index Exercises appear at the end of each chapter.
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on nonparametric Statistical Process control of univariate Processes
Technometrics, 2011Co-Authors: Peihua QiuAbstract:This article considers Statistical Process control (SPC) of univariate Processes when the parametric form of the Process distribution is unavailable. Most existing SPC procedures are based on the assumption that a parametric form (e.g., normal) of the Process distribution can be specified beforehand. In the literature, it has been demonstrated that their performance is unreliable in cases when the prespecified Process distribution is invalid. To overcome this limitation, some nonparametric (or distribution-free) SPC charts have been proposed, most of which are based on the ordering information of the observed data. This article tries to make two contributions to the nonparametric SPC literature. First, we propose an alternative framework for constructing nonparametric control charts, by first categorizing observed data and then applying categorical data analysis methods to SPC. Under this framework, some new nonparametric control charts are proposed. Second, we compare our proposed control charts with sev...
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multivariate Statistical Process control using lasso
Journal of the American Statistical Association, 2009Co-Authors: Changliang Zou, Peihua QiuAbstract:This article develops a new multivariate Statistical Process control (SPC) methodology based on adapting the LASSO variable selection method to the SPC problem. The LASSO method has the sparsity property of being able to select exactly the set of nonzero regression coefficients in multivariate regression modeling, which is especially useful in cases where the number of nonzero coefficients is small. In multivariate SPC applications, Process mean vectors often shift in a small number of components. Our primary goals are to detect such a shift as soon as it occurs and to identify the shifted mean components. Using this connection between the two problems, we propose a LASSO-based multivariate test statistic, and then integrate this statistic into the multivariate EWMA charting scheme for Phase II multivariate Process monitoring. We show that this approach balances protection against various shift levels and shift directions, and thus provides an effective tool for multivariate SPC applications. This article...
Manabu Kano - One of the best experts on this subject based on the ideXlab platform.
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Statistical Process monitoring based on dissimilarity of Process data
Aiche Journal, 2002Co-Authors: Manabu Kano, Shinji Hasebe, Iori Hashimoto, Hiromu OhnoAbstract:Multivariate Statistical Process control (MSPC) has been widely used for monitoring chemical Processes with highly correlated variables. In this work, a novel Statistical Process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of Process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 Process and the Tennessee Eastman Process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected.
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a new multivariate Statistical Process monitoring method using principal component analysis
Computers & Chemical Engineering, 2001Co-Authors: Manabu Kano, Shinji Hasebe, Iori Hashimoto, Hiromu OhnoAbstract:Abstract Principal component analysis (PCA) has been used successfully as a multivariate Statistical Process control (MSPC) tool for detecting faults in Processes with highly correlated variables. In the present work, a novel Statistical Process monitoring method is proposed for further improvement of monitoring performance. It is termed ‘moving principal component analysis’ (MPCA) because PCA is applied on-line by moving the time-window. In MPCA, changes in the direction of each principal component or changes in the subspace spanned by several principal components are monitored. In other words, changes in the correlation structure of Process variables, instead of changes in the scores of predefined principal components, are monitored by using MPCA. The monitoring performance of the proposed method and that of the conventional MSPC method are compared with application to simulated data obtained from a simple 2×2 Process and the Tennessee Eastman Process. The results clearly show that the monitoring performance of MPCA is considerably better than that of the conventional MSPC method and that dynamic monitoring is superior to static monitoring.
Charles R. Farrar - One of the best experts on this subject based on the ideXlab platform.
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structural health monitoring using Statistical Process control
Journal of Structural Engineering-asce, 2000Co-Authors: Hoon Sohn, Jerry A. Czarnecki, Charles R. FarrarAbstract:This paper poses the Process of structural health monitoring in the context of a Statistical pattern recognition paradigm. This paper particularly focuses on applying a Statistical Process control ...
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Structural Health Monitoring Using Statistical Process Control
Journal of Structural Engineering, 2000Co-Authors: Hoon Sohn, Jerry A. Czarnecki, Charles R. FarrarAbstract:Thispaper poses the Process of structural health monitoring in thecontext of a Statistical pattern recognition paradigm. This paper particularlyfocuses on applying a Statistical Process control (SPC) technique knownas an "X-bar control chart" to vibration-based damage diagnosis. Acontrol chart provides a Statistical framework for monitoring future measurementsand for identifying new data that are inconsistent with pastdata. First, an autoregressive (AR) model is fit to themeasured time histories from an undamaged structure. Coefficients of theAR model are selected as the damage-sensitive features for thesubsequent control chart analysis. Next, control limits of the X-barcontrol chart are constructed based on the features obtained fromthe initial structure. Finally, the AR coefficients of the modelsfit to subsequent new data are monitored relative to thecontrol limits. A Statistically significant number of features outside thecontrol limits indicate a system transition from a healthy stateto a damage state. A unique aspect of this studyis the coupling of various projection techniques such as principalcomponent analysis and linear and quadratic discriminant operators with theSPC in an effort to enhance the discrimination between featuresfrom the undamaged and damaged structures. This combined Statistical procedureis applied to vibration test data acquired from a concretebridge column as the column is progressively damaged. The coupledapproach captures a clearer distinction between undamaged and damaged vibrationresponses than by applying an SPC alone.
A L Boyer - One of the best experts on this subject based on the ideXlab platform.
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Statistical Process control for radiotherapy quality assurance
Medical Physics, 2005Co-Authors: Todd Pawlicki, Matthew L Whitaker, A L BoyerAbstract:Every quality assurance Process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, Statistical Process control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of Process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using Process behavior charts were able to identify systematic changes in our daily quality assurance Process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the Process. The Process behavior thresholds calculated from a subset of themore » data detected a 2% change in the Process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the Process.« less
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Statistical Process control for radiotherapy quality assurance
Medical Physics, 2005Co-Authors: Todd Pawlicki, Matthew L Whitaker, A L BoyerAbstract:Every quality assurance Process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, Statistical Process control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of Process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using Process behavior charts were able to identify systematic changes in our daily quality assurance Process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the Process. The Process behavior thresholds calculated from a subset of the data detected a 2% change in the Process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the Process.
