Fault diagnosis in dynamic systems using reinforcement learning

dc.contributor.advisorFernandez, Benito R.
dc.creatorPany, Rajeev Vadakicharla
dc.date.accessioned2022-06-08T00:48:11Z
dc.date.available2022-06-08T00:48:11Z
dc.date.issued1995
dc.description.abstractThis thesis is an effort to extend the concepts of reinforcement learning to Fault Diagnosis and Detection. The focus will be in trying to detect incipient faults in dynamic systems which are incompletely specified, or which have unmodeled and uncertain dynamics, or in the presence of noise. The method of Temporal Differences has been used to train a recurrent multi-layer perceptron architecture, known as an adaptive critic to predict the incipient faults occurring in dynamic systems. The effort is in trying to detect and classify faults based on their dynamics rather than just the states, and hence be in a position to make early predictions on the final fault condition. This paradigm has been demonstrated by applying the scheme to a second order spring-mass-damper system and then extending it to a fourth order continuous stirred tank reactor. Additionally, we have also presented an analysis scheme based on Lie Algebra and the concepts of observability of nonlinear systems, to ensure the detectability and distinguishability of faults. The fault detection scheme presented within can be used in tandem with any analytical redundancy technique such as the Luenberger observer, Kalman filter, Parity space or the parameter estimation techniqueen_US
dc.description.departmentMechanical Engineeringen_US
dc.format.mediumelectronicen_US
dc.identifier.urihttps://hdl.handle.net/2152/114508
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/41411
dc.language.isoengen_US
dc.relation.ispartofUT Electronic Theses and Dissertationsen_US
dc.rightsCopyright © is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en_US
dc.rights.restrictionRestricteden_US
dc.subjectReinforcement learningen_US
dc.subjectFault diagnosisen_US
dc.subjectFault detectionen_US
dc.subjectFault locationen_US
dc.subjectDynamic systemsen_US
dc.subject.lcshReinforcement learning
dc.subject.lcshFault location (Engineering)
dc.titleFault diagnosis in dynamic systems using reinforcement learningen_US
dc.typeThesisen_US
dc.type.genreThesisen_US
thesis.degree.departmentMechanical Engineeringen_US
thesis.degree.disciplineMechanical Engineeringen_US
thesis.degree.grantorUniversity of Texas at Austinen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science in Engineeringen_US

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