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Multivariate Statistical Process Control (multivariate + statistical_process_control)
Selected AbstractsStatistical process monitoring based on dissimilarity of process dataAICHE JOURNAL, Issue 6 2002Manabu Kano 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. [source] Application of latent variable methods to process control and multivariate statistical process control in industryINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2005Theodora Kourti Abstract Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re-starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd. [source] Integration of colour and textural information in multivariate image analysis: defect detection and classification issuesJOURNAL OF CHEMOMETRICS, Issue 1-2 2007J. M. Prats-Montalbán Abstract In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling,,,) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] Framework for regression-based missing data imputation methods in on-line MSPCJOURNAL OF CHEMOMETRICS, Issue 8 2005Francisco Arteaga Abstract Missing data are a critical issue in on-line multivariate statistical process control (MSPC). Among the different scores estimation methods for future multivariate incomplete observations from an existing principal component analysis (PCA) model, the most statistical efficient ones are those that estimate the scores for the new incomplete observation as the prediction from a regression model. We have called them regression-based methods. Several approximations have been proposed in the literature to overcome the singularity or ill-conditioning problems that some of the mentioned methods can suffer due to missing data. This is particularly acute in on-line batch process monitoring. In order to ease the comparison of the statistical performance of these methods and to improve the understanding of their relationships, in this paper we propose a framework that allows to write these regression-based methods by an unique expression, function of a key matrix. From this framework a statistical performance index (PRESV) is introduced as a way to compare the statistical efficiency of the different framework members and to predict the impact of specific missing data combinations on scores estimation without requiring real data. The results are illustrated by application to several continuous and batch industrial data sets. Copyright © 2005 John Wiley & Sons, Ltd. [source] Principal-component analysis of multiscale data for process monitoring and fault diagnosisAICHE JOURNAL, Issue 11 2004Seongkyu Yoon Abstract An approach is presented to multivariate statistical process control (MSPC) for process monitoring and fault diagnosis based on principal-component analysis (PCA) models of multiscale data. Process measurements, representing the cumulative effects of many underlying process phenomena, are decomposed by applying multiresolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales, and PCA is applied to these multiscale data to capture process variable correlations occurring at different scales. Choosing an orthonormal mother wavelet allows each principal component to be a function of the process variables at only one scale level. The proposed method is discussed in the context of other multiscale approaches, and illustrated in detail using simulated data from a continuous stirred tank reactor (CSTR) system. A major contribution of the paper is to extend fault isolation methods based on contribution plots to multiscale approaches. In particular, once a fault is detected, the contributions of the variations at each scale to the fault are computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, one can further compute the variable contributions to those scales, thereby making fault diagnosis much easier. A comparison study is done through Monte Carlo simulation. The proposed method can enhance fault detection and isolation (FDI) performance when the frequency content of a fault effect is confined to a narrow-frequency band. However, when the fault frequency content is not localized, the multiscale approaches perform very comparably to the standard single-scale approaches, and offer no real advantage. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2891,2903, 2004 [source] Multivariate statistical process control charts: an overviewQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2007S. Bersimis Abstract In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Hybrid SPC Method with the Chi-Square Distance Monitoring Procedure for Large-scale, Complex Process DataQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2006Nong Ye Abstract Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T2, cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square () Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data. Copyright © 2005 John Wiley & Sons, Ltd. [source] | |||||||||||||