Data driven process monitoring based on neural networks and classification trees

Show simple item record


dc.contributor.advisor Hahn , Juergen en_US
dc.contributor.committeeMember West , Harry H . en_US
dc.creator Zhou , Yifeng en_US
dc.date.accessioned 2005 -11 -01T15 :50 :47Z
dc.date.accessioned 2014 -02 -19T19 :23 :07Z
dc.date.available 2005 -11 -01T15 :50 :47Z
dc.date.available 2014 -02 -19T19 :23 :07Z
dc.date.created 2004 -08 en_US
dc.date.issued 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

Citation

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

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search

Browse