Show simple item record

dc.contributor.authorHemmer, Martin
dc.contributor.authorKlausen, Andreas
dc.contributor.authorHuynh, Khang
dc.contributor.authorRobbersmyr, Kjell Gunnar
dc.contributor.authorWaag, Tor Inge
dc.identifier.citationIEEE Access. 2020
dc.description.abstractThis 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. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds.
dc.rightsCC BY 4.0
dc.titleHealth Indicator for Low-speed Axial Bearings using Variational Autoencoders
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holder© 2020, Authors
dc.source.journalIEEE Access

Files in this item


This item appears in the following Collection(s)

Show simple item record

CC BY 4.0
Except where otherwise noted, this item's license is described as CC BY 4.0