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Selecting the best process variables for classification of production batches into quality levels
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Selecting the best process variables for classification of production batches into quality levels
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ETD_1902
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051776
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eng
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theses
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Topic
Industrial and Systems Engineering
Subject
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(authority = ETD-LCSH)
Topic
Quality control
Subject
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(authority = ETD-LCSH)
Topic
Multivariate analysis
Abstract
In chemical and industrial processes hundreds of noisy and correlated process variables are collected for process monitoring. This volume of data makes it hard to identify the key variables. Typically, the goal has been to identify important process variables and create a model using these to predict product variables.
The main contribution of this research is a methodology to identify the process variables leading to the best categorization of production batches into classes regarding some final specification, like quality or profitability.
The proposed method, Variable-k-Pareto (VKP), applies a multivariate regression to historical data to establish relations between the process and product variables. Parameters of the multivariate regression generate indices that rank the process variables according to their importance for classification purposes. A data mining technique is then applied on the process variables, classification performance is evaluated, and the variable ranked as the least important is eliminated. This classification/elimination procedure is repeated until a lower bound of remaining variables is reached. A graph relating classification performance and percent of retained variables is generated and the Pareto-Optimal analysis identifies the best subset of variables for classification purposes.
The method performs remarkably well on real and simulated data, retaining only average 5 to 10 percent of the original variables, while significantly increasing the classification performance. We also test alternative classification techniques, as Probabilistic Neural Network and Support Vector Machine, but the k-Nearest Neighbor performs better.
The VKP method is easily adapted to situations where several classification performance measures, e.g., sensitivity and specificity, are needed for batch classification. The cost of collecting variables can also be used. The method also performs well when the product variable falls into more than two quality classes.
Another contribution is the improvement of the VKP method for variable selection in batches where the product variable is located near the cut off limit. Such product variables are highly affected by measurement error, reducing the precision of batch classification. The resulting method shows that batches with product variable near the cut off limit require a particular subset of process variables for classification, different from the subset for the remaining batches.
The main contribution of this research is a methodology to identify the process variables leading to the best categorization of production batches into classes regarding some final specification, like quality or profitability.
The proposed method, Variable-k-Pareto (VKP), applies a multivariate regression to historical data to establish relations between the process and product variables. Parameters of the multivariate regression generate indices that rank the process variables according to their importance for classification purposes. A data mining technique is then applied on the process variables, classification performance is evaluated, and the variable ranked as the least important is eliminated. This classification/elimination procedure is repeated until a lower bound of remaining variables is reached. A graph relating classification performance and percent of retained variables is generated and the Pareto-Optimal analysis identifies the best subset of variables for classification purposes.
The method performs remarkably well on real and simulated data, retaining only average 5 to 10 percent of the original variables, while significantly increasing the classification performance. We also test alternative classification techniques, as Probabilistic Neural Network and Support Vector Machine, but the k-Nearest Neighbor performs better.
The VKP method is easily adapted to situations where several classification performance measures, e.g., sensitivity and specificity, are needed for batch classification. The cost of collecting variables can also be used. The method also performs well when the product variable falls into more than two quality classes.
Another contribution is the improvement of the VKP method for variable selection in batches where the product variable is located near the cut off limit. Such product variables are highly affected by measurement error, reducing the precision of batch classification. The resulting method shows that batches with product variable near the cut off limit require a particular subset of process variables for classification, different from the subset for the remaining batches.
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electronic resource
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x, 96 p. : ill.
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Ph.D.
Note
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Includes bibliographical references (p. 78-84)
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by Michel Jose Anzanello
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Anzanello
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Michel Jose
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1978
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Michel Jose Anzanello
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Albin
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Susan
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Susan Lee Albin
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Coit
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David
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David W. Coit
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Chaovalitwongse
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Wanpracha
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Wanpracha Art Chaovalitwongse
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Flavio
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Flavio S. Fogliatto
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3B56JXX
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ETD doctoral
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The author owns the copyright to this work
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Open
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Anzanello
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Michel
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Michel Anzanello
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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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