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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.
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https://hal-supelec.archives-ouvertes.fr/hal-00658069
Contributor : Yanfu Li <>
Submitted on : Thursday, January 12, 2012 - 3:22:39 PM
Last modification on : Tuesday, October 20, 2020 - 10:49:15 AM
Long-term archiving on: : Friday, April 13, 2012 - 2:21:16 AM

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  • HAL Id : hal-00658069, version 1

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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⟩

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