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v2 (29-03-2012)

Probabilistic outlier detection in vibration spectra with small learning dataset

Aurélien Hazan 1, Michel Verleysen 2, 3, Marie Cottrell 2, Jérôme Lacaille 4, Kurosh Madani 1

(31/03/2012)

The issue of detecting abnormal vibrations from spectra 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 vibration measured from a bearing test rig and from an aircraft engine, we show that when only a small learning set is available, probabilistic approaches have several advantages, including modelling healthy vibrations, and thus ensuring fault detection. To do so, we compare two original algorithms: the first one relies on the statistics of the maximum of log-periodograms. The second one computes the probability density function (pdf) of the wavelet transform of log-periodograms, and a likelihood index when new periodograms are presented. A by-product of it is the ability to generate random log-periodograms according with respect to the learning dataset. Receiver Operator Characteristic (ROC) curves are built in several experimental settings, and show the superiority of one of our algorithms over state-of-the-art machine-learning-oriented fault detection methods; lastly we generate random samples of aircraft engine log-periodograms.

1 :	Laboratoire Image, Signaux et Systèmes Intelligents (LISSI - EA 3956)
	Université Paris-Est Créteil Val-de-Marne (UPEC)
2 :	Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM)
	Université Paris I - Panthéon-Sorbonne
3 :	Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM)
	Université Catholique de Louvain (UCL) - Belgique
4 :	SNECMA Villaroche [Moissy-Cramayel]
	Safran Group

Équipe de recherche : MSI LISSI

Domaine

:

Sciences de l'ingénieur/Traitement du signal et de l'image Informatique/Traitement du signal et de l'image Statistiques/Machine Learning

Mots Clés : fault detection – vibration – rotating machine – bearing – aircraft engine – bayesian inference – periodogram – maximum – excess – wavelet – generalized pareto – probabilistic model

hal-00681036, version 2

http://hal.archives-ouvertes.fr/hal-00681036

oai:hal.archives-ouvertes.fr:hal-00681036

Contributeur : Aurélien Hazan <>

Soumis le : Mercredi 28 Mars 2012, 18:37:36

Dernière modification le : Jeudi 29 Mars 2012, 10:21:15