An unsupervised autoencoder framework with a temporal prediction block constructs dynamic health indicators that model degradation trends in bearing signals for improved prognostics.
Gonz ´alez-Mu˜niz, I
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
An Unsupervised Framework for Dynamic Health Indicator Construction and Its Application in Rolling Bearing Prognostics
An unsupervised autoencoder framework with a temporal prediction block constructs dynamic health indicators that model degradation trends in bearing signals for improved prognostics.