Original paper
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
Abstract
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the...
Paper Details
Title
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
Published Date
Oct 1, 2019
Journal
Volume
125
Pages
113100 - 113100
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