Bayesian inference for outlier detection in vibration spectra with small learning dataset - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Bayesian inference for outlier detection in vibration spectra with small learning dataset

Résumé

The issue of detecting abnormal vibrations is addressed in this article, when little is known both on the mechanical behavior of the system, and on the characteristic patterns of potential faults. With data from a bearing test rig and from an aircraft engine, we show that when only a small learning set is available, Bayesian inference has several advantages in order to compute a model of healthy vibrations, and thus ensure fault detection. To do so, we compute the wavelet transform of many log-periodograms, and show that their probability density can be easily modelled. This allows us to compute a likelihood index when a new log-periodogram is presented, thanks to marginal likelihood approximation. A by-product of this computation is the ability to generate random log-periodograms according to the learning dataset probability density. Finally, we first detect the degradation of a bearing on a test rig; then we generate random samples of aircraft engine log-periodograms.
Fichier principal
Vignette du fichier
spec_fault_detec_bayes_wavelet_surv6.pdf (270.72 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00660793 , version 1 (17-01-2012)

Identifiants

  • HAL Id : hal-00660793 , version 1

Citer

Aurélien Hazan, Michel M. Verleysen, Marie Cottrell, Jérôme Lacaille. Bayesian inference for outlier detection in vibration spectra with small learning dataset. Surveillance 6, Oct 2011, Compiègne, France. http://www.surveillance6.fr/. ⟨hal-00660793⟩
169 Consultations
126 Téléchargements

Partager

Gmail Facebook X LinkedIn More