An unsupervised autoencoder framework with a temporal prediction block constructs dynamic health indicators that model degradation trends in bearing signals for improved prognostics.
Zio, Prognostics and health management (phm): Where are we and where do we (need to) go in theory and practice, Reliability Engineering & System Safety 218 (2022) 108119
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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.