Digital Twin for Prognostics: Analysis, Simulation, and Experiment

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dc.contributor.advisorGhorbel, Fathi Hen_US
dc.creatorBrownell, Kenneth Clayen_US
dc.date.accessioned2023-08-09T18:06:24Zen_US
dc.date.available2023-08-09T18:06:24Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-04-21en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-08-09T18:06:24Zen_US
dc.description.abstractThe emerging research on Digital Twins (DTs) for prognostics has been vital to the future of Industry 4.0. These DTs can be utilized to increase the accuracy and efficiency for prognostics and health management (PHM) for complex industrial equipment. This is based on the approximation of the Remaining Useful Life (RUL) which requires an accurate digital twin that represents the dynamics of the physical system. However, the dynamics for the physical system do not remain constant due to structural changes over time. Research by our group on the application of an equation discovery technique known as Model Error Discovery with Interpretability and Data Assimilation (MEDIDA) for DTs has proven feasible for structural identification and model updates. This thesis analyzes the elements of a DT for prognostics through structural identification for an Industrial Emulator (IE) experimental setup. A closed-loop system was determined necessary for structural identification and was used to successfully identify a single nonlinearity and a nonlinearity caused by frictional dynamics of the LuGre friction model with an additional unmeasurable state. These elements are crucial to obtain a DT that replicates the physical system and provides a more accurate RUL calculation for prognostics.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBrownell, Kenneth Clay. "Digital Twin for Prognostics: Analysis, Simulation, and Experiment." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/115129">https://hdl.handle.net/1911/115129</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/115129en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectDigital Twinen_US
dc.subjectPrognosticsen_US
dc.subjectStructural Discoveryen_US
dc.subjectEquation Discoveryen_US
dc.titleDigital Twin for Prognostics: Analysis, Simulation, and Experimenten_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMechanical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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