Learning Shape for Jet Engine Novelty Detection

  • David A. Clifton
  • Peter R. Bannister
  • Lionel Tarassenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Application of a neural network approach to data exploration and the generation of a model of system normality is described for use in novelty detection of vibration characteristics of a modern jet engine. The analysis of the shape of engine vibration signatures is shown to improve upon existing methods of engine vibration testing, in which engine vibrations are conventionally compared with a fixed vibration threshold. A refinement of the concept of “novelty scoring” in this approach is also presented.


Cluster Centre Vibration Signature Radial Basis Function Neural Network Engine Vibration Novelty Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David A. Clifton
    • 1
    • 2
  • Peter R. Bannister
    • 1
  • Lionel Tarassenko
    • 1
  1. 1.Department of Engineering ScienceOxford UniversityUK
  2. 2.Magdalen CentreOxford BioSignals Ltd.OxfordUK

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