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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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statistical Monitoring of Complex Multivatiate processes 1

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丛书名： Statistics in Practice

2012年

作者： Kruger Uwe Xie Lei

ISBN: (数字)9781118381267

ISBN: (纸本)9780470028193

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. statistical Monitoring of Complex multivariate processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry. This book: Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

关键词： multivariate analysis multivariate processes statistical monitoring power engineering multivariate statistical techniques mechanical engineering manufacturing engineering multivariate statistical process control

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A BRIEF INTRODUCTION TO BASIC MULTIV ARIATE ECONOMIC statistical process control

A BRIEF INTRODUCTION TO BASIC MULTIV ARIATE ECONOMIC STATIST...

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作者： Precious Mudavanhu University of Stellenbosch

学位级别：硕士

statistical process control (SPC) plays a very important role in monitoring and improving industrial processes to ensure that products produced or shipped to the customer meet the required specifications. The main tool that is used in SPC is the statistical control chart. The traditional way of statistical control chart design assumed that a process is described by a single quality characteristic. However, according to Montgomery and Klatt (1972) industrial processes and products can have more than one quality characteristic and their joint effect describes product quality. process monitoring in which several related variables are of interest is referred to as multivariate statistical process control (MSPC). The most vital and commonly used tool in MSPC is the statistical control chart as in the case of the SPC. The design of a control chart requires the user to select three parameters which are: sample size, n, sampling interval, h and control limits,. k Several authors have developed control charts based on more than one quality characteristic, among them was Hotelling (1947) who pioneered the use of the multivariate process control techniques through the development of a 2 -control T chart which is well known as Hotelling 2 -control T chart. Since the introduction of the control chart technique, the most common and widely used method of control chart design was the statistical design. However, according to Montgomery (2005), the design of control has economic implications. There are costs that are incurred during the design of a control chart and these are: costs of sampling and testing, costs associated with investigating an out-of-control signal and possible correction of any assignable cause found, costs associated with the production of nonconforming products, etc. The paper is about giving an overview of the different methods or techniques that have been employed to develop the different economic statistical models for MSPC. The first multivariate economic m

关键词： control chart statistical process control multivariate statistical process control multivariate economic statistical process control multivariate control chart loss function

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multivariate process control by transition scheme in soft- drink process using 3-Way PLS approach

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Procedia Food Science 2011年 1卷 1181-1187页

作者： Jorge Saavedra Andres Córdova Research Group on Chemometrics Department of Food Engineering Pontificia Universidad Católica de Valparaíso Waddington 716 Valparaíso2360021 Chile Centro Regional de Estudios en Alimentos Saludables (CREAS) Blanco 1623 of 1402 Edificio Torres Mar del Sur II Valparaíso 2362396 Chile

Recently many attentions has been placed to process transitions, startups and restarts, since these abnormalities often arrives to the loss of production time, undesirable and not detectable variability increasing withproduction of off- grade materials. The present research deal with the modeling a continuous soft-drinks bottling process by a multivariate approach based on the 3-Way PLS. process was modeled, filtering the high grade of autocorrelation and cross-correlation within the studied variables. The responsible variables for that behavior were detected by 3-Way PLS which could be useful to separate autocorrelated sources of variability enable to control this kind of industrial process.

关键词： multivariate statistical process control Transitions 3-Way PLS Soft-drink bottling process PCA MPLS

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Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第3期61卷 437-446页

作者： Ben Khediri, Issam Limam, Mohamed Weihs, Claus Dortmund Univ Technol Dept Stat Dortmund Germany Univ Tunis Inst Super Gest Lab Operat Res Decis & Control Tunis Tunisia

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multivariate statistical process control Block adaptive Kernel Principal Component Analysis Variable window control chart

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Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing processes

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2011年第3期24卷 432-444页

作者： Yu, Jianbo Shanghai Univ Dept Mech Automat Engn Shanghai 200072 Peoples R China

Fault detection has been recognized in the semiconductor industry as an effective component of advanced process control framework in increasing yield and product quality. Recently, conventional process monitoring-based principal component analysis (PCA) has been applied to semiconductor manufacturing by quickly detecting when the process abnormalities have occurred. However, the unique characteristics of the semiconductor processes, nonlinearity in most batch processes, multimodal batch trajectories due to multiple operating conditions, significantly limit the applicability of PCA to the fault detection of semiconductor manufacturing. To explicitly address these unique issues in semiconductor processes, a principal components (PCs)-based Gaussian mixture model (GMM) (named PCGMM) has been proposed in this paper. GMM is capable to handle complicated data with nonlinearity or multimodal features by a mixture of multiple Gaussian components, which is very suitable to describe observations from semiconductor processes. Furthermore, two quantification indexes (negative Log likelihood probability and Mahalanobis distance) are proposed for assessing process states, and a Bayesian inference-based calculation method is further used to provide the process failure probability. The validity and effectiveness of PCGMM-based fault detection model are illustrated through two simulation processes and a semiconductor manufacturing process. The experimental results demonstrated that the proposed model is superior to PCA-based monitoring models and can achieve accurate and early detection of various types of faults in complicated manufacturing processes.

关键词： Fault detection Gaussian mixture model multivariate statistical process control principal component analysis semiconductor manufacturing process

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Integrating independent component analysis and local outlier factor for plant-wide process monitoring

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JOURNAL OF process control 2011年第7期21卷 1011-1021页

作者： Lee, Jaeshin Kang, Bokyoung Kang, Suk-Ho Seoul Natl Univ Dept Ind Engn Seoul 151742 South Korea

We propose a novel process monitoring method integrating independent component analysis (ICA) and local outlier factor (LOF). LOF is a recently developed outlier detection technique which is a density-based outlierness calculation method. In the proposed monitoring scheme, ICA transformation is performed and the control limit of LOF value is obtained based on the normal operating condition (NOC) dataset. Then, at the monitoring phase, the LOF value of current observation is computed at each monitoring time, which determines whether the current process is a fault or not. The comparison experiments are conducted with existing ICA-based monitoring schemes on widely used benchmark processes, a simple multivariate process and the Tennessee Eastman process. The proposed scheme shows the improved accuracy over existing schemes. By adopting LOF, the monitoring statistic is computed regardless of data distribution. Therefore, the proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non-Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Local outlier factor Independent component analysis multivariate statistical process control Fault detection process monitoring Tennessee Eastman process

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A distribution-free method for process monitoring

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9821-9829页

作者： Ge, Zhiqiang Song, Zhihuan Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Proc Control Hangzhou 310027 Zhejiang Peoples R China

Traditional multivariate statistical process control methods such as principal component analysis are limited to Gaussian process data when they used for process monitoring. However, the deficiency is not due to the method itself, but lies in the monitoring statistic construction and its confidence limit determination. This paper proposed a distribution-free method, which employs the one-class SVM to construct new monitoring statistics. Thus two new statistics are developed separately in two subspaces of the PCA model: the principal component subspace and the residual subspace. When some fault has been detected, a novel fault reconstruction scheme is proposed. For fault identification, two new identification indices are constructed. The performance of the proposed method in fault detection, reconstruction and identification is evaluated through a case study of the Tennessee Eastman (TE) benchmark process. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fault detection Fault reconstruction Fault identification One-class SVM multivariate statistical process control

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In-line monitoring of alcohol precipitation by near-infrared spectroscopy in conjunction with multivariate batch modeling

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ANALYTICA CHIMICA ACTA 2011年第1期707卷 47-56页

作者： Huang, Hongxia Qu, Haibin Zhejiang Univ Coll Pharmaceut Sci Pharmaceut Informat Inst Hangzhou 310058 Zhejiang Peoples R China

Alcohol precipitation is a critical unit operation during the manufacture of Chinese herbal injections. To facilitate enhanced process understanding and develop control strategy, the use of near-infrared spectroscopy (NIRS) combined with multivariate statistical process control (MSPC) methodology was investigated for in-line monitoring of alcohol precipitation. The effectiveness of the proposed approach was evaluated through an experimental campaign. Six batches were run under normal operating conditions to study batch-to-batch variation or batch reproducibility and establish MSPC control limits, while artificial process variations were purposefully introduced into the four test batches to assess the capability of the model for real-time fault detection. Several MSPC tools were compared and assessed. NIRS, in conjunction with MSPC, has proven to be a feasible process analytical technology (PAT) tool for monitoring batch evolution and potentially facilitating model-based advanced process control of the alcohol precipitation during the manufacture of Chinese herbal injections. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Batch-to-batch reproducibility Chinese herbal injection Alcohol precipitation multivariate statistical process control multivariate data analysis Near-infrared spectroscopy

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A nonparametric fault isolation approach through one-class classification algorithms

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IIE TRANSACTIONS 2011年第7期43卷 505-517页

作者： Kim, Seoung Bum Sukchotrat, Thuntee Park, Sun-Kyoung Korea Univ Sch Ind Management Engn Seoul South Korea Hanyang Cyber Univ Sch Business Adm Seoul South Korea Hanyang Cyber Univ Sch Business Adm Seoul 13379 South Korea Korea Univ Sch Ind Management Engn Seoul South Korea

multivariate control charts provide control limits for the monitoring of processes and detection of abnormal events so that processes can be improved. However, these multivariate control charts provide limited information about the contribution of any specific variable to the out-of-control alarm. Although many fault isolation methods have been developed to address this deficiency, most of these methods require a parametric distributional assumption that restricts their applicability to specific problems of process control and thus limits their broader usefulness. This study proposes a nonparametric fault isolation method based on a one-class classification algorithm that overcomes the limitation posed by the parametric assumption in existing fault isolation methods. The proposed approach decomposes the monitoring statistics obtained from a one-class classification algorithm into components that reflect the contribution of each variable to the out-of-control signal. A bootstrap approach is used to determine the significance of each variable. A simulation study is presented that examines the performance of the proposed method under various scenarios and to results are compared with those obtained using the T2 decomposition method. The simulation results reveal that the proposed method outperforms the T2 decomposition method in non-normal distribution cases.

关键词： Decomposition fault isolation multivariate statistical process control nonparametric one-class classification algorithm

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