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Determination of Multi-Mode Component Failure and Time-To-Failure with Machine Learning and Deep Learning

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dc.contributorFonseca, Daniel
dc.contributorSazonov, Edward
dc.contributorVikas, Vishesh
dc.contributorWilliams, Keith
dc.contributor.advisorYoon, Hwan-Sik
dc.contributor.authorO'Donnell, John Lewis
dc.date.accessioned2023-08-03T18:42:23Z
dc.date.available2028-06-01
dc.date.issued2023
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractA hybrid deep learning and machine learning approach for both failure mode classification and time-to-failure prediction is proposed in this dissertation, with a focus on a multi-mode failure regime where the state of health of components can vary. To validate the potential performance of the proposed approach, a vehicle's leaking hydraulic suspension system is simulated via a quarter car model as a proof of concept. Next, the quarter car model is expanded to include a model of a failing engine mount. The dynamic data from these models is employed to train a NARX net with the health condition and degradation rate as an output. The predictive capabilities of the NARX given this data is significant, validating the proposed approach. To develop the hybrid approach, a four-cylinder diesel engine with EGR and VGT is simulated over a prescribed operational range for four failure types. A multi-label CNN model is utilized to classify which multi-mode failure modes are occurring while hiding health condition information. The approach resulted in significant performance in classifying the failure modes. This data is then employed in regression models to determine the health condition of various components. It was determined that utilizing this data and previous state information with ensemble tree methods and neural networks results in predictive accuracy with less than 3% normalized root mean square error. Finally, a NARX net approach for determining the degradation rate and time-to-failure of a component utilizing this health condition information is verified. A discussion on how these approaches can be combined to create a hybrid predictive model that determines the engine's probable failing time is presented. Based on the results from each stage of the hybrid model, it is expected that this approach can provide significant predictive performance in monitoring the health of a diesel engine as well as any similar system with comparable failure modes.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otherhttp://purl.lib.ua.edu/187835
dc.identifier.otheru0015_0000001_0004657
dc.identifier.otherODonnell_alatus_0004D_15197
dc.identifier.urihttps://ir.ua.edu/handle/123456789/10470
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.
dc.subjectDeep Learning
dc.subjectDegradation
dc.subjectFailure
dc.subjectMachine Learning
dc.subjectMulti-Mode
dc.subjectRemaining Useful Life
dc.titleDetermination of Multi-Mode Component Failure and Time-To-Failure with Machine Learning and Deep Learningen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Aerospace Engineering and Mechanics
etdms.degree.disciplineMechanical engineering
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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