Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra

A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to nov(cid:173) elty detection provide a measure of how unusual the shape of a vibra(cid:173) tion 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 appli(cid:173) cation to Jet Engine vibration analysis.