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dc.contributor.advisorBryant, Michael Daviden
dc.creatorRengarajan, Sankar Bharathien
dc.date.accessioned2011-02-16T20:16:05Zen
dc.date.accessioned2011-02-16T20:16:23Zen
dc.date.available2011-02-16T20:16:05Zen
dc.date.available2011-02-16T20:16:23Zen
dc.date.issued2010-12en
dc.date.submittedDecember 2010en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2501en
dc.descriptiontexten
dc.description.abstractModel based fault detection techniques utilize functional redundancies in the static and dynamic relationships among system inputs and outputs for fault detection and isolation. Analytical models based on the underlying physics of the system can capture the dependencies between different measured signals in terms of system states and parameters. These physical models of the system can be used as a tool to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. These error residuals contain valuable information to interpret system states and parameters. Setting up the measurements from a faulty system as baseline, the parameters of the idealistic model can be varied to minimize these residuals. This process is called “Parameter Tuning”. A framework to automate this “Parameter Tuning” process is presented with a focus on DC motors and 3-phase induction motors. The parameter tuning module presented is a multi-tier module which is designed to operate on real system models that are highly non-linear. The tuning module combines artificial intelligence techniques like Quasi-Monte Carlo (QMC) sampling (Hammersley sequencing) and Genetic Algorithm (Non Dominated Sorting Genetic Algorithm) with an Extended Kalman filter (EKF), which utilizes the system dynamics information available via the physical models of the system. A tentative Graphical User Interface (GUI) was developed to simplify the interaction between a machine operator and the module. The tuning module was tested with real measurements from a DC motor. A simulation study was performed on a 3-phase induction motor by suitably adjusting parameters in an analytical model. The QMC sampling and genetic algorithm stages worked well even on measurement data with the system operating in steady state condition. But the downside was computational expense and inability to estimate the parameters online – ‘batch estimator’. The EKF module enabled online estimation where update was made based on incoming measurements. But observability of the system based on incoming measurements posed a major challenge while dealing with state estimation filters. Implementation details and results are included with plots comparing real and faulty systems.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectModel based diagnosticsen
dc.subjectParameter estimationen
dc.subjectExtended Kalman filteren
dc.subjectGenetic algorithmen
dc.subjectHammersley samplingen
dc.titleA method for parameter estimation and system identification for model based diagnosticsen
dc.date.updated2011-02-16T20:16:23Zen
dc.contributor.committeeMemberAkella, Maruthi R.en
dc.description.departmentMechanical Engineeringen
dc.type.genrethesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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