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

Abstract : 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.
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Communication dans un congrès
Surveillance 6, Oct 2011, Compiègne, France., 2011
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Contributeur : Aurélien Hazan <>
Soumis le : mardi 17 janvier 2012 - 16:36:09
Dernière modification le : mercredi 6 juin 2018 - 17:52:23
Document(s) archivé(s) le : mercredi 18 avril 2012 - 02:41:17


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  • HAL Id : hal-00660793, version 1


Aurélien Hazan, Michel Verleysen, Marie Cottrell, Jérôme Lacaille. Bayesian inference for outlier detection in vibration spectra with small learning dataset. Surveillance 6, Oct 2011, Compiègne, France., 2011. 〈hal-00660793〉



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