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Analysis of process control baseline data using data mining
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Analysis of process control baseline data using data mining
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Zhang
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Hang
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Hang Zhang
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Albin
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Susan
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Advisory Committee
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Susan L Albin
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Chaovalitwongse
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Wanpracha
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Advisory Committee
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Wanpracha Chaovalitwongse
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Xu
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Di
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Advisory Committee
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Di Xu
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Zhang
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Hang
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1975-
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Hang Zhang
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
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English
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electronic
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x, 123 pages
Abstract
There are two phases in multivariate statistical process control (MSPC). In phase I, we model baseline data off-line to characterize the process. Baseline data is a collection of observations describing successful manufacturing. In phase II, we compare on-line observations to these models to determine whether the process is in control. This dissertation addresses four questions to improve phase I analysis: (1) How many operational modes are in baseline data? (2) In a large historical dataset collected over a long time period, which periods are the baseline? (3) In profile baseline data, are there outlier profiles? (4) When should the phase I model be updated?
Each operational mode appears as a cluster in baseline data. To address the first question, we propose a new method to determine the number of clusters with all of the following critical features: it determines if there is only one cluster, the most common case; it identifies convex or non-convex clusters; and it is insensitive to user-specified parameters. No existing method has them all. Simulations show that the proposed method works well.
We propose a new method to address the second question, where historical data may be collected during both baseline and unsuccessful periods. The identified baseline periods are reasonably long, and have the best product quality with a stable distribution. Through simulated and real datasets, the proposed method shows its robustness to various distributions, in contrast to the existing change point identification method that is very sensitive to the distribution.
We address the third question in the context of complex profiles. We treat complex profiles as high-dimension vectors. We apply the control chart to identify outliers. Applied to simulated and real datasets, it demonstrates better performance on complex profiles than the existing nonlinear regression method.
We address the fourth question by testing whether the correlation matrix changes from the baseline. The correlation matrix describes relationships among variables. We propose a new method to diagnose the responsible variables when the change is indicated.
We also discuss the future work of applying MSPC and data mining technologies on data from a brain neural system.
Each operational mode appears as a cluster in baseline data. To address the first question, we propose a new method to determine the number of clusters with all of the following critical features: it determines if there is only one cluster, the most common case; it identifies convex or non-convex clusters; and it is insensitive to user-specified parameters. No existing method has them all. Simulations show that the proposed method works well.
We propose a new method to address the second question, where historical data may be collected during both baseline and unsuccessful periods. The identified baseline periods are reasonably long, and have the best product quality with a stable distribution. Through simulated and real datasets, the proposed method shows its robustness to various distributions, in contrast to the existing change point identification method that is very sensitive to the distribution.
We address the third question in the context of complex profiles. We treat complex profiles as high-dimension vectors. We apply the control chart to identify outliers. Applied to simulated and real datasets, it demonstrates better performance on complex profiles than the existing nonlinear regression method.
We address the fourth question by testing whether the correlation matrix changes from the baseline. The correlation matrix describes relationships among variables. We propose a new method to diagnose the responsible variables when the change is indicated.
We also discuss the future work of applying MSPC and data mining technologies on data from a brain neural system.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 118-122).
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Industrial and Systems Engineering
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Process control--Statistical methods
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Data mining
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Graduate School - New Brunswick Electronic Theses and Dissertations
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16803
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doi:10.7282/T34Q7VCH
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ETD doctoral
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Hang Zhang
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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