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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Published in:	IEEE access Vol. 8; pp. 35842 - 35852
Main Authors:	Hemmer, Martin, Klausen, Andreas, Khang, Huynh Van, Robbersmyr, Kjell G., Waag, Tor I.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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. 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.
AbstractList	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. 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.
Author	Robbersmyr, Kjell G. Waag, Tor I. Hemmer, Martin Khang, Huynh Van Klausen, Andreas
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SubjectTerms	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
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Title	Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
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