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Support vector novelty detection applied to jet engine vibration spectra

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Hayton, P., Schölkopf, B., Tarassenko, L., & Anuzis, P. (2001). Support vector novelty detection applied to jet engine vibration spectra. Advances in Neural Information Processing Systems, 946-946.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E2B2-7
Zusammenfassung
A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to novelty detection provide a measure of how unusual the shape of a vibration signature is, by learning a representation of normality. We describe a novel method for Support Vector Machines of including information from a second class for novelty detection and give results from the application to Jet Engine vibration analysis.