Naïve Bayesian Classifier for On-line Remaining Useful Life Prediction of Degrading Bearings - CentraleSupélec Access content directly
Conference Papers Year : 2011

Naïve Bayesian Classifier for On-line Remaining Useful Life Prediction of Degrading Bearings

Abstract

In this paper, the estimation of the Residual Useful Life (RUL) of degraded thrust ball bearings is made resorting to a data-driven stochastic approach that relies on an iterative Naïve Bayesian Classifier (NBC) for regression task. NBC is a simple stochastic classifier based on applying Bayes' theorem for posterior estimate updating. Indeed, the implemented iterative procedure allows for updating the RUL estimation based on new information collected by sensors located on the degrading bearing, and is suitable for an on-line monitoring of the component health status. The feasibility of the approach is shown with respect to real world vibration-based degradation data.
Fichier principal
Vignette du fichier
Anno_2011_3.pdf (391.07 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00658069 , version 1 (12-01-2012)

Identifiers

  • HAL Id : hal-00658069 , version 1

Cite

Francesco Di Maio, Selina S. Y. Ng, Kwok-Leung Tsui, Enrico Zio. Naïve Bayesian Classifier for On-line Remaining Useful Life Prediction of Degrading Bearings. MMR2011, Jun 2011, China. pp.1-14. ⟨hal-00658069⟩
127 View
622 Download

Share

Gmail Facebook Twitter LinkedIn More