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...
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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.
This paper describes a case study in which multivariatestatistical procedures have been developed to assist in the supervision of an industrial fed-batch fermentation process operated by Biochemie in Austria. The pro...
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This paper describes a case study in which multivariatestatistical procedures have been developed to assist in the supervision of an industrial fed-batch fermentation process operated by Biochemie in Austria. The procedures have been developed to enhance the monitoring capabilities of the current system by interfacing directly into the present G2 real-time knowledge based supervisory system. While the G2 rule based system is useful for detecting deviations in single variables, it has been found to be unable to detect some of the more subtle deviations caused by the complex interactions between the process variables. multivariatestatistical techniques have been utilised in this study to provide early indications of deviations from nominal batch behaviour. The cause of these deviations can subsequently be determined by interrogating the information produced by these algorithms. Although the multivariatestatistical techniques adopted in this paper are not new, their integration within the industrial supervisory system and the on-line application to the industrial fermentation process is novel.
multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a ...
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multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an (1) 'intuitive' univariate approach, and (2) with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis (PCA) method can be displayed graphically in multivariatecontrol charts, multivariate Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking (SMART) chart, is introduced and used. (C) 1998 Elsevier Science B.V. All rights reserved.
This paper describes a case study in which multivariatestatistical procedures have been developed to assist in the supervision of an industrial fed-batch fermentation process. Currently supervisory control of the ind...
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This paper describes a case study in which multivariatestatistical 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.
A non-linear principal components analysis algorithm is proposed for process performance monitoring. Before assessing the capabilities of the monitoring scheme on an industrial dryer, the data is first pre-processed t...
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A non-linear principal components analysis algorithm is proposed for process performance monitoring. Before assessing the capabilities of the monitoring scheme on an industrial dryer, the data is first pre-processed through application of a wavelet filter to remove noise and spikes. The wavelet coefficients are then used as the inputs into the non-linear algorithm. Performance monitoring charts with non-parametric control limits were then constructed to identify the occurrence of non-conforming operation prior to interrogating non-linear contribution plots to help identify the potential source of the fault. Encouraging results were achieved.
Principal component analysis (PCA) has been applied widely for monitoring plant performance across a range of industrial processes. PCA is a linear technique and it is therefore not strictly applicable for handling in...
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Principal component analysis (PCA) has been applied widely for monitoring plant performance across a range of industrial processes. PCA is a linear technique and it is therefore not strictly applicable for handling industrial problems which exhibit significant non-linear behaviour. A novel non-linear PCA method is proposed based upon the Input-Training neural network. multivariate statistical process control charts with non-parametric control limits are then defined to overcome the limitations of the conventional approach of defining the limits based upon the assumption of normality. A contribution plot capable of identifying the potential source of the fault in a non-linear situation is then proposed prior to applying the methodology to a continuous industrial reactor. (C) 1998 Elsevier Science Ltd. All rights reserved.
In this article, we present a method for monitoring multivariateprocess data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use reduction to two di...
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In this article, we present a method for monitoring multivariateprocess data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use reduction to two dimensions for displaying the state of the process but all the data for determining whether it is in a state of statisticalcontrol, This approach allows us to detect changes in location, variation, and correlational structure accurately yet display a large amount of information concisely, We illustrate the use of the biplot on an example of industrial data and also discuss some of the issues related to a practical implementation of the method.
statisticalprocesscontrol (SPC) provides a tool for achieving and maintaining product quality. In today's climate of major data monitoring campaigns there has been an increase in interest in the multivariate sta...
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statisticalprocesscontrol (SPC) provides a tool for achieving and maintaining product quality. In today's climate of major data monitoring campaigns there has been an increase in interest in the multivariatestatistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. Within univariate SPC, techniques for identifying when a process is moving out of control are well established. Similar guidelines are required for multivariate statistical process control (MSPC). Two approaches will be discussed - Hotelling's T-2 statistic and a new approach, the M(2) statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor. Copyright (C) 1996 Elsevier Science Ltd
statisticalprocesscontrol (SPC) provides a tool for achieving and maintaining product quality. Traditional methods have focused on the monitoring of one quality variable at a time. However, in todays climate of majo...
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statisticalprocesscontrol (SPC) provides a tool for achieving and maintaining product quality. Traditional methods have focused on the monitoring of one quality variable at a time. However, in todays climate of major data monitoring exercises this approach is unrealistic and as a result there has been an increase in interest in the multivariatestatistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. There still remain a number of questions which require to be addressed before these new approaches to monitoring can be widely adopted by industry. Within univariate SPC, techniques for identifying when the process is moving out of control are well established and are based both on sound statistical practice and engineering knowledge built up over a number of years. Similar underpinning guidelines require to be established for multivariate statistical process control (MSPC). Two approaches to this problem of defining control limits will be discussed - those based upon Hotelling's T2 statistic and a new approach based upon kernel density estimation and the standard bootstrap, the M2 statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor.
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