DEEP ADVERSARIAL APPROACHES IN RELIABILITY

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dc.contributor.advisorModarres, Mohammaden_US
dc.contributor.authorVerstraete, David Benjaminen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-07-10T05:32:56Z
dc.date.available2020-07-10T05:32:56Z
dc.date.issued2020en_US
dc.description.abstractReliability engineering has long been proposed with the problem of predicting failures using all available data. As modeling techniques have become more sophisticated, so too have the data sources from which reliability engineers can draw conclusions. The Internet of Things (IoT) and cheap sensing technologies have ushered in a new expansive set of multi-dimensional big machinery data in which previous reliability engineering modeling techniques remain ill-equipped to handle. Therefore, the objective of this dissertation is to develop and advance reliability engineering research by proposing four comprehensive deep learning methodologies to handle these big machinery data sets. In this dissertation, a supervised fault diagnostic deep learning approach with applications to the rolling element bearings incorporating a deep convolutional neural network on time-frequency images was developed. A semi-supervised generative adversarial networks-based approach to fault diagnostics using the same time-frequency images was proposed. The time-frequency images were used again in the development of an unsupervised generative adversarial network-based methodology for fault diagnostics. Finally, to advance the studies of remaining useful life prediction, a mathematical formulation and subsequent methodology to combine variational autoencoders and generative adversarial networks within a state-space modeling framework to achieve both unsupervised and semi-supervised remaining useful life estimation was proposed. All four proposed contributions showed state of the art results for both fault diagnostics and remaining useful life estimation. While this research utilized publicly available rolling element bearings and turbofan engine data sets, this research is intended to be a comprehensive approach such that it can be applied to a data set of the engineer’s chosen field. This research highlights the potential for deep learning-based approaches within reliability engineering problems.en_US
dc.identifierhttps://doi.org/10.13016/nb4r-ro3y
dc.identifier.urihttp://hdl.handle.net/1903/26187
dc.language.isoenen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pquncontrolledDeep Learningen_US
dc.subject.pquncontrolledDiagnosticsen_US
dc.subject.pquncontrolledPrognosticsen_US
dc.subject.pquncontrolledReliabilityen_US
dc.titleDEEP ADVERSARIAL APPROACHES IN RELIABILITYen_US
dc.typeDissertationen_US

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