Degradation Vector Fields with Uncertainty Considerations
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 (DV...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/93343 |
| _version_ | 1848765725827661824 |
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| author | Star, Marco |
| author_facet | Star, Marco |
| author_sort | Star, Marco |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:39:49Z |
| format | Thesis |
| id | curtin-20.500.11937-93343 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:39:49Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-933432023-09-19T06:32:51Z Degradation Vector Fields with Uncertainty Considerations Star, Marco 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. 2023 Thesis http://hdl.handle.net/20.500.11937/93343 Curtin University fulltext |
| spellingShingle | Star, Marco Degradation Vector Fields with Uncertainty Considerations |
| title | Degradation Vector Fields with Uncertainty Considerations |
| title_full | Degradation Vector Fields with Uncertainty Considerations |
| title_fullStr | Degradation Vector Fields with Uncertainty Considerations |
| title_full_unstemmed | Degradation Vector Fields with Uncertainty Considerations |
| title_short | Degradation Vector Fields with Uncertainty Considerations |
| title_sort | degradation vector fields with uncertainty considerations |
| url | http://hdl.handle.net/20.500.11937/93343 |