Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Fur...

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Vydané v:	IEEE access Ročník 8; s. 35842 - 35852
Hlavní autori:	Hemmer, Martin, Klausen, Andreas, Khang, Huynh Van, Robbersmyr, Kjell G., Waag, Tor I.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autoregressive models Autoregressive processes Bearing fault detection condition monitoring conditional variational autoencoder Data points Datasets Decoding Dimensionality reduction Drilling machines Drilling machines (tools) False alarms Fault detection generative models Industries Low speed Offshore drilling rigs Roller bearings Rolling bearings Training unsupervised learning variational autoencoder
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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