Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
(2023) Uncertainty-aware remaining useful life prediction for predic- tive maintenance using deep learning, Procedia CIRP 118, 116–12
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.
citing papers explorer
-
Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
-
Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
A hybrid framework bifurcates RUL prediction for turbofan engines into healthy and degraded regimes via LSTM autoencoder state classification, using Weibull survival analysis and probabilistic neural networks with MC dropout for uncertainty-aware estimates on the C-MAPSS dataset.