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Bayesian inference for outlier detection in vibration spectra with small learning dataset
- Source :
- Proceedings of Surveillance 6, Surveillance 6, Surveillance 6, Oct 2011, Compiègne, France. http://www.surveillance6.fr/, Surveillance 6, Oct 2011, Compiègne, France. http://www.surveillance6.fr/, 2011
- Publication Year :
- 2011
- Publisher :
- HAL CCSD, 2011.
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Abstract
- International audience; 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.
- Subjects :
- [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]
[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Proceedings of Surveillance 6, Surveillance 6, Surveillance 6, Oct 2011, Compiègne, France. http://www.surveillance6.fr/, Surveillance 6, Oct 2011, Compiègne, France. http://www.surveillance6.fr/, 2011
- Accession number :
- edsair.dedup.wf.001..edebb925382373f177b8c2a253805b7f