A new turbofan dataset with realistic maintenance patterns is used to benchmark Bayesian filters as strong baselines against self-supervised learning representations for component health estimation.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
A new turbofan dataset with realistic maintenance patterns is used to benchmark Bayesian filters as strong baselines against self-supervised learning representations for component health estimation.