Data driven process monitoring based on neural networks and classification trees

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dc.contributor.advisor Hahn , Juergen en_US
dc.contributor.committeeMember West , Harry H . en_US
dc.creator Zhou , Yifeng en_US 2005 -11 -01T15 :50 :47Z 2014 -02 -19T19 :23 :07Z 2005 -11 -01T15 :50 :47Z 2014 -02 -19T19 :23 :07Z 2004 -08 en_US 2005 -11 -01T15 :50 :47Z
dc.identifier.uri http : / /hdl .handle .net /1969 .1 /2740
dc.description.abstract Process monitoring in the chemical and other process industries has been of great practical importance . Early detection of faults is critical in avoiding product quality deterioration , equipment damage , and personal injury . The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems . Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems . However , due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes , their applications have been difficult . The first part of this work tackles this problem by employing a polynomial -based data preprocessing step that greatly reduces the dimensionality of the neural network process model . The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients , which are usually of far lower dimensionality than the original data , are used to build a neural network model to produce residuals for fault classification . Case studies show a significant reduction in neural model construction time and sometimes better classification results as well . The second part of this research investigates classification trees as a promising approach to fault detection and classification . It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems , and construction time can excessive for high dimensional problems . Fisher Discriminant Analysis (FDA ) , which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores , is used as a dimensionality reduction tool . Classification trees use the scores to separate observations into different fault classes . A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order . Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem . en_US
dc.format.extent 1859620 bytes
dc.format.medium electronic en_US
dc.format.mimetype application /pdf
dc.language.iso en _US en_US
dc.publisher Texas A &M University en_US
dc.subject Process monitoring en_US
dc.title Data driven process monitoring based on neural networks and classification trees en_US
dc.type Book en
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US


Data driven process monitoring based on neural networks and classification trees. Available electronically from http : / /hdl .handle .net /1969 .1 /2740 .

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