Visualization of multivariate process data for fault detection and diagnosis

dc.contributor.advisorBaldea, Michael
dc.contributor.advisorEdgar, Thomas F.
dc.creatorWang, Ray Chenen
dc.date.accessioned2014-10-02T20:58:08Zen
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.date.updated2014-10-02T20:58:09Zen
dc.descriptiontexten
dc.description.abstractThis report introduces the concept of three-dimensional (3D) radial plots for the visualization of multivariate large scale datasets in plant operations. A key concept of this representation of data is the introduction of time as the third dimension in a two dimensional radial plot, which allows for the display of time series data in any number of process variables. This report shows the ability of 3D radial plots to conduct systemic fault detection and classification in chemical processes through the use of confidence ellipses, which capture the desired operating region of process variables during a defined period of steady-state operation. Principal component analysis (PCA) is incorporated into the method to reduce multivariate interactions and the dimensionality of the data. The method is applied to two case studies with systemic faults present (compressor surge and column flooding) as well as data obtained from the Tennessee Eastman simulator, which contained localized faults. Fault classification using the interior angles of the radial plots is also demonstrated in the paper.en
dc.description.departmentChemical Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/26246en
dc.language.isoenen
dc.subjectVisualizationen
dc.subjectBig dataen
dc.subjectFault detectionen
dc.titleVisualization of multivariate process data for fault detection and diagnosisen
dc.typeThesisen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineChemical Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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