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Model predictive control monitoring using multivariate statistics

JOURNAL OF process control 2009年第2期19卷 314-327页

作者： AlGhazzawi, Ashraf Lennox, Barry Univ Manchester Control Syst Ctr Sch Elect & Elect Engn Manchester M13 9PL Lancs England

control loop monitoring has become an important research field over the past decade. Research has primarily targeted single-input single-output (SISO) feedback control systems with limited progress being made on the monitoring of multi-input multi-output (MIMO) control systems and large scale model predictive control (MPC) systems in particular. The size and complexity of MPC systems means that identifying and diagnosing problems with their operation can be challenging. This paper presents an MPC condition monitoring tool based on multivariate statistical process control (MSPC) techniques. The proposed tool uses intuitive charts to enable casual users of MPC technology to detect abnormal controller operation and to identify possible causes for this behaviour. Through its application to data collected from a large scale MPC system, the proposed technique is shown to be able to identify and diagnose poor control performance resulting from various issues including inappropriate interaction by process operators. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： control condition monitoring multivariate statistical process control Model predictive control Principal component analysis (PCA) Partial least squares (PLS) Recursive PCA and PLS Condensate fractionation

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Diagnosing manufacturing variation using second-order and fourth-order statistics

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INTERNATIONAL JOURNAL OF FLEXIBLE MANUFACTURING SYSTEMS 2004年第1期16卷 45-64页

作者： Lee, HY Apley, DW Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Texas A&M Univ Dept Ind Engn College Stn TX 77843 USA

This article discusses a method that can aid in diagnosing root causes of product and process variability in complex manufacturing processes, when large amounts of multivariate in-process measurement data are available. A linear structured model, similar to the standard factor analysis model, is used to generically represent the variation patterns that result from the root causes. Blind source separation techniques form the basis for identifying the precise characteristics of each individual variation pattern in order to facilitate the identification of their root causes. The second-order and fourth-order statistics that are used in various blind separation algorithms are combined in an optimal manner to form a more effective and black-box method with wider applicability.

关键词： blind source separation factor rotation manufacturing variation multivariate statistical process control principal components analysis

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Design strategies for the multivariate exponentially weighted moving average control chart

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2004年第6期20卷 571-577页

作者： Testik, MC Borror, CM Cukurova Univ Dept Ind Engn TR-01330 Adana Turkey Arizona State Univ Dept Ind Engn Tempe AZ 85287 USA

The multivariate exponentially weighted moving average (MEWHA) control chart has received significant attention from researchers and practitioners because of its desirable properties. There are several different approaches to the design of MEWHA control charts: statistical design;economic-statistical design;and robust design. In this paper a review and comparison of these design strategies is provided. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： multivariate statistical process control multivariate exponentially weighted moving average economic design statistical design robustness

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A New Degradation Indicator Based on a statistical Anomaly Approach

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IEEE TRANSACTIONS ON RELIABILITY 2016年第1期65卷 326-335页

作者： Pordeus Gomes, Joao Paulo Harrop Galvao, Roberto Kawakami Yoneyama, Takashi Leao, Bruno P. Univ Fed Ceara Dept Comp Sci BR-60440900 Fortaleza Ceara Brazil Inst Tecnol Aeronaut Dept Elect Engn BR-12229900 Sao Jose Dos Campos SP Brazil GE Global Res Brazil Technol Ctr BR-21941615 Rio De Janeiro RJ Brazil

This paper presents a new method for combining measured parameters into a single indicator for monitoring the condition of systems subjected to degradation effects. The proposed approach integrates the use of nonparametric density estimation techniques into Runger's method, which allows for the separation of variables that are directly related to degradation effects from those which are not. Two simulated case studies are presented for illustration, namely, the monitoring of a flap extension and retraction system, and a gas turbine employed as an auxiliary power unit. For comparison, degradation indicators are also calculated by using Hotteling's and Runger's methods, as well as a nonparametric method without separation of variables. In both case studies, the proposed method provided the best results in terms of fault detection performance and suitability for remaining useful life prediction.

关键词： Fault detection multivariate statistical process control nonparametric methods

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Monitoring correlation within linear profiles using mixed models

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JOURNAL OF QUALITY TECHNOLOGY 2008年第2期40卷 167-183页

作者： Jensen, Willis A. Birch, Jeffrey B. Woodall, William H. WL Gore & Assoc Inc Med Products Div Flagstaff AZ 86003 USA Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA

Profile monitoring is a relatively new set of techniques in quality control used when the product or process quality is best represented by a function (or a curve) at each time period. The idea is often to model the profile via some parametric method and then monitor the estimated parameters over time to determine if there have been changes in the profiles. Previous modeling methods have not incorporated a correlation structure within the profiles. We propose the use of linear mixed models to monitor the linear profiles in order to account for any correlation structure within a profile. We conclude that, when the data are balanced, there appears to be no advantage in modeling correlation and/or including random effects because a simpler analysis that ignores the correlation structure will perform just as well as the more complicated analysis. When the data are unbalanced or when there are missing data, we find that the linear mixed model approach is preferable to an approach that ignores the correlation structure. Our focus is on Phase I control-chart applications.

关键词： eblups missing data mixed models multivariate statistical process control phase 1 T-2 statistic

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Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry

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COMPUTERS & CHEMICAL ENGINEERING 2008年第1-2期32卷 12-24页

作者： Kano, Manabu Nakagawa, Yoshiaki Kyoto Univ Dept Chem Engn Nishikyo Ku Kyoto 6158510 Japan Sumitomo Met Kokura Ltd Kokurakita Ku Kitakyushu Fukuoka 8028686 Japan

The issue of how to improve product quality and product yield in a brief period of time becomes more critical in many industries. Even though industrial processes are totally different in appearance, the problems to solve are highly similar: how to build a reliable model from a limited data, how to analyze the model and relate it to first principles, how to optimize operating condition, and how to realize an on-line monitoring and control system and maintain it. In this paper, statistical process monitoring and control methodologies are briefly surveyed, and our application results in steel facilities are presented. The achievements of the present work are as follows: (1) the development of a new method that can cope with qualitative quality information and relate operating conditions to product quality or product yield, (2) the simultaneous analysis of multiple processing units including a converter, a continuous caster, a blooming process, and rolling processes, and (3) the successful application results in the steel industry. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： soft-sensor multivariate statistical process control multivariate analysis iron and steel process quality improvement quantification

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multivariate statistical monitoring of multiphase batch processes with between-phase transitions and uneven operation durations

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2012年第6期90卷 1383-1392页

作者： Yao, Yuan Dong, Weiwei Zhao, Luping Gao, Furong Natl Tsing Hua Univ Dept Chem Engn Hsinchu Taiwan Hong Kong Univ Sci & Technol Ctr Polymer Proc & Syst Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China

In order to achieve satisfactory monitoring, multivariate statistical process models should well reflect process nature. In manufacturing systems, many batch processes are inherently multiphase. Usually, different phases have different characteristics, while gradual transitions are often observed between phases. Another important feature of batch processes is the unevenness of operation durations. Especially, in multiphase batch processes, the situation becomes more complicated. In this study, a batch process modelling and monitoring strategy is proposed based on Gaussian mixture model (GMM), which can automatically extract phase and transition information for uneven-duration batch processes. The application results verify the effectiveness of the proposed method. (c) 2011 Canadian Society for Chemical Engineering

关键词： multivariate statistical process control fault detection Gaussian mixture model multiphase uneven operation duration

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A High-dimensional control Chart for Profile Monitoring

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2011年第4期27卷 451-464页

作者： Chen, Shuohui Nembhard, Harriet Black Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16801 USA

Profile monitoring is an important and rapidly emerging area of statistical process control. In many industries, the quality of processes or products can be characterized by a profile that describes a relationship or a function between a response variable and one or more independent variables. A change in the profile relationship can indicate a change in the quality characteristic of the process or product and, therefore, needs to be monitored for control purposes. We propose a high-dimensional (HD) control chart approach for profile monitoring that is based on the adaptive Neyman test statistic for the coefficients of discrete Fourier transform of profiles. We investigate both linear and nonlinear profiles, and we study the robustness of the HD control chart for monitoring profiles with stationary noise. We apply our control chart to monitor the process of nonlinear woodboard vertical density profile data of Walker and Wright (J. Qual. Technol. 2002;34: 118-129) and compare the results with those presented in Williams et al. (Qual. Reliab. Eng. Int. 2007;to appear). Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： adaptive Neyman test statistic autoregressive models discrete Fourier transform multivariate statistical process control profile data time series models

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Chromatographic Peak Alignment Using Derivative Dynamic Time Warping

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BIOTECHNOLOGY PROGRESS 2013年第2期29卷 394-402页

作者： Bork, Christopher Ng, Kenneth Liu, Yinhan Yee, Alex Pohlscheidt, Michael Genentech Inc Mfg Sci & Technol Oceanside CA 92056 USA Univ Minnesota Dept Chem Engn Minneapolis MN 55455 USA Stanford Univ Dept Bioengn Stanford CA 94304 USA

Chromatogram overlays are frequently used to monitor inter-batch performance of bioprocess purification steps. However, the objective analysis of chromatograms is difficult due to peak shifts caused by variable phase durations or unexpected process holds. Furthermore, synchronization of batch process data may also be required prior to performing multivariate analysis techniques. Dynamic time warping was originally developed as a method for spoken word recognition, but shows potential in the objective analysis of time variant signals, such as manufacturing data. In this work we will discuss the application of dynamic time warping with a derivative weighting function to align chromatograms to facilitate process monitoring and fault detection. In addition, we will demonstrate the utility of this method as a preprocessing step for multivariate model development. (c) 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29: 394402, 2013

关键词： chromatography chromatogram alignment time warping multivariate statistical process control batch monitoring

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Decomposition of Scatter Ratios Used in Monitoring multivariate process Variability

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2010年第12期39卷 2128-2145页

作者： Mason, Robert L. Chou, Youn-Min Young, John C. SW Res Inst San Antonio TX 78228 USA Univ Texas San Antonio San Antonio TX USA

Wilks' ratio statistic can be defined in terms of the ratio of the sample generalized variances of two non-independent estimators of the same covariance matrix. Recently this statistic has been proposed as a control statistic for monitoring changes in the covariance matrix of a multivariate normal process in a Phase II situation, particularly when the dimension is larger than the sample size. In this article we derive a technique for decomposing Wilks' ratio statistic into the product of independent factors that can be associated with the components of the covariance matrix. With these results, we demonstrate that, when a signal is detected in a control procedure for the Phase II monitoring of process variability using the ratio statistic, the signaling value can be decomposed and the process variables contributing to the signal can be specifically identified.

关键词： multivariate statistical process control Sample generalized variance Signal interpretation

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