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Application of latent variable methods to process control and multivariate statistical process control in industry

INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL processING 2005年第4期19卷 213-246页

作者： Kourti, T McMaster Univ Dept Chem Engn McMaster Adv Control Consortium Hamilton ON L8S 4L7 Canada

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 (c) 2005 John Wiley & Sons, Ltd.

关键词： multivariate statistical process control PLS PCA batch process monitoring adaptive PLS soft sensors fault detection fault isolation data archiving data compression image analysis

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Fault detection and diagnosis with random forest feature extraction and variable importance methods

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IFAC Proceedings Volumes 2010年第9期43卷 79-86页

作者： C. Aldrich L. Auret Department of Process Engineering University of Stellenbosch RSA (Tel: +2721-808-4487) Department of Process Engineering University of Stellenbosch RSA

The ever-present drive to safer, more cost-effective and cleaner processes motivates the exploration of a variety of process monitoring methods. In the domain of data-driven approaches, random forest models present a nonlinear framework. Random forest models consist of ensembles of classification and regression trees in which the model response is determined by voting committees of independent binary decision trees. Data-driven approaches to fault diagnosis often involve summarizing potentially large numbers of process variables in lower dimensional diagnostic sequences. Random forest feature extraction allows for the monitoring of process in feature and residual spaces, while random forest variable importance measures can potentially be used to identify process variables contribution to fault conditions. In this study, a framework for diagnosing steady state faults with random forests is proposed and demonstrated with a simple nonlinear system and the benchmark Tennessee Eastman process.

关键词： Random forest models feature extraction variable importance fault diagnosis multivariate statistical process control

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multivariate SPC of Emulsion and Nanoparticle Slurry processes Based on process Tomography,Dynamic Light Scattering and Acoustic Spectroscopic Data

Multivariate SPC of Emulsion and Nanoparticle Slurry Process...

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中国颗粒学会第七届学术年会暨海峡两岸颗粒技术研讨会

作者： Bundit Boonkhao Richard Tweedie Ken Primrosc Jason Corbett Fraser McNeil-Watson Institute of Particle Science and Engineering University of LeedsLeeds LS2 9JT

Manufacture of nanometre particulate products in suspensions is becoming increasingly important to the pharmaceutical,speciality chemical,and functional materials *** instance,nano-processing is now used as an effective drug-delivery method for solid form hydrophobic pharmaceuticals due to the dramatically increased drug solubility and bioavailability at *** biggest challenge to nano-processing under industrial conditions has repeatedly highlighted as the difficulty in achieving consistency in product quality as characterised by particle size / size distribution and ***-line process analytical technology is considered as an enabling technique to improve process control and develop product quality assurance strategies.

关键词： multivariate statistical process control dynamic light scattering acoustic spectroscopy process tomography

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High-Dimensional process Monitoring and Fault Isolation via Variable Selection

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JOURNAL OF QUALITY TECHNOLOGY 2009年第3期41卷 247-258页

作者： Wang, Kaibo Jiang, Wei Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Kowloon Hong Kong Peoples R China

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables;a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

关键词： Forward Selection Linear Regression multivariate statistical process control T-2 Chart

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Phase and transition based batch process modeling and online monitoring

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JOURNAL OF process control 2009年第5期19卷 816-826页

作者： Yao, Yuan Gao, Furong Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China

Multiple phases/stages with transitions from phase to phase are important characteristics of many batch processes. In order to model and monitor batch processes more accurately and efficiently, such process features are needed to be considered carefully. In this work, an index based on the angles between different principal component analysis (PCA) score spaces is developed to quantify the similarities between PCA models. Phase division algorithm is designed based on this new PCA similarity index, following by a statistical transition identification step. The steady phase ranges and transition ranges are then modeled separately. The transition models can be calculated by solving the optimization problems. Application examples show the advantages of the proposed method on both batch process modeling and online monitoring. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Batch process multivariate statistical process control PCA process modeling process monitoring Multiphase Multistage

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Moving window kernel PCA for adaptive monitoring of nonlinear processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2009年第2期96卷 132-143页

作者： Liu, Xueqin Kruger, Uwe Littler, Tim Xie, Lei Wang, Shuqing Petr Inst Dept Elect Engn Abu Dhabi U Arab Emirates Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a Computation complexity of O(N-2), whilst batch techniques, e.g. the Lanczos method, are of O(N-3). Including the adaptation of the number of retained components and an I-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Nonlinear process Kernel PCA Moving window multivariate statistical process control Adaptive Numerically efficient

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A multivariate Robust control Chart for Individual Observations

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JOURNAL OF QUALITY TECHNOLOGY 2009年第3期41卷 259-271页

作者： Chenouri, Shoja'eddin Steiner, Stefan H. Variyath, Asokan Mulayath Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Mem Univ Newfoundland Dept Math & Stat St John NF A1C 5S7 Canada

To monitor a multivariate process, a classical Hotelling's T-2 control chart is often used. However, it is well known that such control charts are very sensitive to the presence of outlying observations in the historical Phase I data used to set the control limit. In this paper, we propose a robust Hotelling's T-2-type control chart for individual observations based on highly robust and efficient estimators of the mean vector and covariance matrix known. as reweighted minimum covariance determinant (RMCD) estimators. We illustrate how to set the control limit for the proposed control chart, study its performance using simulations, and illustrate implementation in a real-world example.

关键词： Hotelling's T-2 multivariate Robust Estimation of Location and Scatter multivariate statistical process control Outliers Reweighted Minimum Covariance Determinant Estimators

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Monitoring Variation in a multivariate process When the Dimension is Large Relative to the Sample Size

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2009年第6期38卷 939-951页

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

A control procedure is presented for monitoring changes in variation for a multivariate normal process in a Phase II operation where the subgroup size, m, is less than p, the number of variates. The methodology is based on a form of Wilk' statistic, which can be expressed as a function of the ratio of the determinants of two separate estimates of the covariance matrix. One estimate is based on the historical data set from Phase I and the other is based on an augmented data set including new data obtained in Phase II. The proposed statistic is shown to be distributed as the product of independent beta distributions that can be approximated using either a chi-square or F-distribution. An ARL study of the statistic is presented for a range of conditions for the population covariance matrix. Cases are considered where a p-variate process is being monitored using a sample of m observations per subgroup and mp. Data from an industrial multivariate process is used to illustrate the proposed technique.

关键词： multivariate statistical process control Sample generalized variance Scatter matrix T2 statistic Wilk' statistic

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NIR techniques create added values for the pellet and biofuel industry

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BIORESOURCE TECHNOLOGY 2009年第4期100卷 1589-1594页

作者： Lestander, Torbjoern A. Johnsson, Bo Grothage, Morgan Swedish Univ Agr Sci Unit Biomass Technol & Chem SE-90304 Umea Sweden Casco Adhes AB SE-85013 Sundsvall Sweden

A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% Moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Near infrared spectroscopy Bi-orthogonal partial least squares (BPLS) multivariate calibration multivariate statistical process control process analytical technology (PAT)

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Model predictive control monitoring using multivariate statistics

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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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