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Real-time synchronization with expected distribution of synchronized index for on-line monitoring of uneven multiphase batch process

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COMPUTERS & CHEMICAL ENGINEERING 2021年 154卷 107490-107490页

作者： Lee, Hye Ji Bae, Jaehan Jeong, Dong Hwi Lee, Jong Min Seoul Natl Univ Sch Chem & Biol Engn Inst Chem Proc 1 Gwanak Ro Seoul 08826 South Korea Univ Ulsan Sch Chem Engn 93 Daehak Ro Ulsan 44610 South Korea

Because of the inconsistent batch duration, identifying the progress of an ongoing batch highly affects the performance of the on-line monitoring. In this paper, a real-time synchronization scheme is proposed based on relative proceeding rates, newly defined as the time difference between the trajectory of an ongoing batch and the nominal trajectory. As the relative proceeding rate indicates how early the point attains its desired value compared to the nominal trajectory, the synchronized index is obtained online by estimating the relative proceeding rate. Compared with the conventional methods that optimize the synchronized index with local constraints, the proposed method can estimate its distribution using the relative proceeding rate without applying local constraints. It can reduce synchronization errors and increase the accuracies of the state estimation and fault detection. The utility of the proposed method is verified via a case study of industrial penicillin production with a real-time unmeasurable variable. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： On-line monitoring Multiphase batch process On-line synchronization Fault detection multivariate statistical process control Penicillin fermentation

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MEAN SHIFTS DIAGNOSIS AND IDENTIFICATION IN BIVARIATE process USING LS-SVM BASED PATTERN RECOGNITION MODEL

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2013年第7-8期20卷 453-467页

作者： Cheng Zhi-Qiang Ma Yi-Zhong Bu Jing Song Hua-Ming Nanjing Univ Sci & Technol Dept Management Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Automat Inst Nanjing 210094 Jiangsu Peoples R China

This study develops a least squares support vector machines (LS-SVM) based model for bivariate process to diagnose abnormal patterns of process mean vector, and to help identify abnormal variable(s) when Shewhart-type multivariate control charts based on Hotelling's T 2 are used. On the basis of studying and defining the normal/abnormal patterns of the bivariate process mean shifts, a LS-SVM pattern recognizer is constructed in this model to identify the abnormal variable(s). The model in this study can be a strong supplement of the Shewhart-type multivariate control charts. Furthermore, the LS-SVM techniques introduced in this research can meet the requirements of process abnormalities diagnosis and causes identification under the condition of small sample size. An industrial case application of the proposed model is provided. The performance of the proposed model was evaluated by computing its classification accuracy of the LS-SVM pattern recognizer. Results from simulation case studies indicate that the proposed model is a successful method in identifying the abnormal variable(s) of process mean shifts. The results demonstrate that the proposed method provides an excellent performance of abnormal pattern recognition. Although the proposed model used for identifying the abnormal variable(s) of bivariate process mean shifts is a particular application, the proposed model and methodology here can be potentially applied to multivariate SPC in general.

关键词： multivariate statistical process control least squares support vector machines pattern recognition quality diagnosis bivariate process

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Simultaneous Identification of Premodeled and Unmodeled Variation Patterns

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JOURNAL OF QUALITY TECHNOLOGY 2010年第1期42卷 36-51页

作者： Apley, Daniel W. Lee, Ho Young Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Samsung Seoul South Korea

For purposes of identifying root causes of variation in multivariate manufacturing data, many studies employ a linear structured model. One paradigm involves modeling off-line a set of variation patterns and then attempting to detect the presence or absence of those specific premodeled patterns in a sample of on-line data. In another paradigm, which requires no premodeling, the objective is to discover the nature of any variation patterns that are present, based only on the on-line data sample. In this paper, we present a method that combines the two paradigms and mitigates some of the shortcomings of each. Instead of exhaustively premodeling all potential variation patterns, which is infeasible for many processes, one premodels only the patterns for which modeling is relatively easy. The characteristics of any unmodeled patterns that also happen to be present in the on-line data are discovered automatically, and they are treated in such a manner that their presence does not adversely affect the detection of the premodeled patterns. We illustrate the approach with an example from autobody assembly.

关键词： Blind Source Separation Factor Analysis Independent Components Analysis Manufacturing Variation Reduction multivariate statistical process control

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On the T² control chart with estimated parameters

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2018年第6期15卷 716-729页

作者： Chen, Jiaqi Yang, Hualong Yao, Jianfeng Harbin Inst Technol Dept Math Harbin Heilongjiang Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China

statistical monitoring of multivariate processes is becoming increasingly important in modern manufacturing environments. Typical equipment may have multiple key variables to be measured continuously. Hotelling's T-2 chart was originally applied for monitoring themean vector of multivariate quality measurements. In practical problems, estimated parameters are needed and their use will modify the properties of control charts. The Average Run Length (ARL), an indicator of the performance of the control charts, will be larger when the estimated parameters are used. As one contribution of the paper, we provide a rigorous proof of this phenomenon which has been reported in several empirical studies. Furthermore, in order to design an efficient T-2 chart with estimated parameters, it is necessary to have a method to calculate or approximate the ARL function. An existing approach in the literature is based on extensive Monte-Carlo simulations. In this paper, we propose a novel approach by providing an analytic approximation of the ARL function in the however limited case of univariate observations.

关键词： Average run length estimated parameters multivariate statistical process control T-2 chart

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Monitoring Batch processes with Multiple On-Off Steps in Semiconductor Manufacturing

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JOURNAL OF QUALITY TECHNOLOGY 2011年第2期43卷 142-157页

作者： Lee, Shui-Pin Chao, An-Kuo Tsung, Fugee Wong, David Shan Hill Tseng, Sheng-Tsiang Jang, Shi-Shang Ching Yun Univ Dept Ind Engn & Management Tao Yuan 32097 Taiwan Natl Tsing Hua Univ Inst Stat Hsinchu 30013 Taiwan Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Kowloon Hong Kong Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

A modern semiconductor manufacturing line is made of hundreds of sequential batch-processing stages. Each of these stages consists of many steps carried out by expensive tools, which are monitored by numerous sensors capable of sampling at intervals of seconds. The sensor readings of each run constitute profiles, which can include extremely drastic changes. The heterogeneous variations at different profile points are mainly due to on-off recipe actions at specific points. In addition, the analysis of these profiles is further complicated by long-term trends due to tool aging and short-term effects specific to the first wafer in a lot cycle. statistical process control methods that fail to take these effects into consideration will lead to frequent false alarms. A systematic method is proposed to address these challenges. First, a reference profile is determined for each sensor variable that describes the on-off actions. Next, level shifts of these profiles in each step are established to capture and remove intrinsic variations due to long-term aging trends and the short-term first-wafer effects. The residuals are used to formulate a health index, and this index can be used to monitor the health of the equipment and detect faulty wafers efficiently.

关键词： Batch-processing Stages Health Index Heterogeneous Variation multivariate statistical process control Sensor Data Signal Map

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A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process

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JOURNAL OF CHEMOMETRICS 1999年第3-4期13卷 379-396页

作者： Wise, BM Gallagher, NB Butler, SW White, DD Barna, GG Eigenvector Res Inc Manson WA 98831 USA Texas Instruments Inc Dallas TX 75265 USA

multivariate statistical process control (MSPC) tools have been developed for monitoring a Lam 9600 TCP metal etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibit a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved optical emission spectroscopy (OES) of the plasma and a radio-frequency monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems is presented and the strengths and weaknesses of the methods are discussed, along with the relative advantages of each of the sensor systems. Copyright (C) 1999 John Wiley & Sons, Ltd.

关键词： fault detection measurement selection multivariate statistical process control principal component analysis multiway principal component analysis trilinear decomposition parallel factor analysis

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M-ATTRIVAR: An attribute-variable chart to monitor multivariate process means

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2018年第2期34卷 214-228页

作者： Aparisi, Francisco Ho, Linda Lee Univ Politecn Valencia Valencia Spain Univ Sao Paulo Sao Paulo Brazil

In this paper, an attribute-variable control chart, namely, M-ATTRIVAR, is introduced to monitor possible shifts in a vector of means. The monitoring starts using an attribute chart (classifying the units as approved or not using a gauge) and continues in such a way until a warning signal is given, shifting the control to a variable chart for the next sampling. If the variable chart does not confirm the warning, the monitoring returns to an attribute control. Otherwise, the monitoring remains with the variable chart. Whenever any of the charts (attribute or variable) signals an alarm, the control scheme triggers an alarm. The main advantage of this new proposal is the possibility of judging the state of the process only by the attribute chart most of the time (normally more economical and faster). The performance of the M-ATTRIVAR control chart is compared versus the main competitor (T-2 control chart) in terms of performance detection (out-of-control average run length) but also economically (average sampling cost). The M-ATTRIVAR is always cheaper than T-2, and in many scenarios, it detects quicker process shifts than the T-2 control chart. A numerical example illustrates a practical situation.

关键词： average run length gauge Hotelling's T-2 multivariate statistical process control

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multivariate process Monitoring Using Classical Multidimensional Scaling and Procrustes Analysis

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IFAC Proceedings Volumes 2010年第5期43卷 165-170页

作者： M.Y.M. Yunus J. Zhang School of Chemical Engineering and Advanced Material Newcastle University Newcastle Upon Tyne NE1 7RU UK

This paper presents a new process monitoring framework using multidimensional scaling. The traditional method of multivariate process monitoring is generally base on principal component analysis (PCA) and is carried out by monitoring the fault detection parameters Hotelling's T 2 and squared prediction errors (SPE). Both indexes are derived directly from multivariate scores in the observation sample configurations. This conventional system was found inappropriately used especially in monitoring highly nonlinear multivariate processes leading to a great number of principal components being selected. Alternatively, classical multidimensional scaling (CMDS) is another technique which can be used in compressing multivariate data by using dissimilarity measures for process monitoring. The proposed process monitoring system is developed based on variable relationships and the dissimilarity measures in terms of variable profiles are used in projecting the multivariate scores. A new monitoring index, which is the resultant vector length different between the new and the normal variable profiles, is introduced. Procrustes analysis (PA) is implemented for on-line process monitoring through a moving-window mechanism. The proposed monitoring method is demonstrated on a simulated continuous stirred tank reactor (CSTR) with recycle system. The results show that the proposed system was efficient as well as effective in detecting various abrupt and incipient faults compared to the linear PCA-based scheme.

关键词： multivariate statistical process control process Monitoring Principal Component Analysis Multidimensional Scaling Classical Scaling

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On the control chart with estimated parameters

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Quality Technology & Quantitative Management 2018年第6期15卷 716-729页

作者： Chen, Jiaqi chenjq1016@*** Yang, Hualong Yao, Jianfeng Department of Mathematics Harbin Institute of Technology Harbin China. Department of Statistics and Actuarial Science University of Hong Kong Hong Kong China

statistical monitoring of multivariate processes is becoming increasingly important in modern manufacturing environments. Typical equipment may have multiple key variables to be measured continuously. Hotelling’s chart was originally applied for monitoring the mean vector of multivariate quality measurements. In practical problems, estimated parameters are needed and their use will modify the properties of control charts. The Average Run Length (ARL), an indicator of the performance of the control charts, will be larger when the estimated parameters are used. As one contribution of the paper, we provide a rigorous proof of this phenomenon which has been reported in several empirical studies. Furthermore, in order to design an efficient chart with estimated parameters, it is necessary to have a method to calculate or approximate the ARL function. An existing approach in the literature is based on extensive Monte-Carlo simulations. In this paper, we propose a novel approach by providing an analytic approximation of the ARL function in the however limited case of univariate observations. [ABSTRACT FROM AUTHOR]

关键词： Average run length chart estimated parameters multivariate statistical process control

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multivariate statistical Monitoring Procedures for Fermentation Supervision: An Industrial Case Study

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IFAC Proceedings Volumes 1998年第8期31卷 399-404页

作者： G.A. Montague H.G. Hiden G. Kornfeld Department of Chemical and Process Engineering University of Newcastle Newcastle upon Tyne England NE1 7RU Biochemie Kundl Austria

This paper describes a case study in which multivariate statistical procedures have been developed to assist in the supervision of an industrial fed-batch fermentation process. Currently supervisory control of the industrial fermentation is aided through use of the G2 realtime knowledge based system. The rule based system is complemented by a number of algorithmic methods. While rules are useful for detecting deviations in single variables, complex interactions between fermentation conditions during batch operation can lead to more subtle deviations. One approach that can be used in such circumstances is Multi-way principal component analysis. This provides early indications of deviations from nominal batch process behaviour and subsequently contribution plots can be utilised to assist in identifying the causes.

关键词： fermentation processses fault detection multivariate statistical process control monitoring implementation

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