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dc.contributor.authorStar, Marco
dc.contributor.supervisorKristoffer McKeeen_US
dc.contributor.supervisorIan Howarden_US
dc.contributor.supervisorTomasz Woloszynskien_US
dc.date.accessioned2023-09-19T06:32:51Z
dc.date.available2023-09-19T06:32:51Z
dc.date.issued2023en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/93343
dc.description.abstract

The focus of this work is on capturing uncertainty in remaining useful life (RUL) estimates for machinery and constructing some latent dynamics that aid in interpreting those results. This is primarily achieved through sequential deep generative models known as Dynamical Variational Autoencoders (DVAEs). These allow for the construction of latent dynamics related to the RUL estimates while being a probabilistic model that can quantify the uncertainties of the estimates.

en_US
dc.publisherCurtin Universityen_US
dc.titleDegradation Vector Fields with Uncertainty Considerationsen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Civil and Mechanical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidStar, Marco [0000-0003-0547-1525]en_US


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