Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components
This work proposes a joint-probabilistic model between the remaining life and inspection observations, which is then used to perform prognostics on currently installed assets. At every new observation, the forward-looking belief on the asset's remaining life is Bayesian updated, granting dynamic estimations on its failure probability. Consequent…
Development of an tool to guide maintenance decision making for a variety of machines. Assignment during the Asset Management course at the University of Groningen
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