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Learning Shape for Jet Engine Novelty Detection

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Department of Engineering Science, Oxford University, UK

    David A. Clifton, Peter R. Bannister & Lionel Tarassenko

  2. Magdalen Centre, Oxford BioSignals Ltd., Oxford Science Park, Oxford, OX4 4GA, UK

    David A. Clifton

Authors
  1. David A. Clifton
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  2. Peter R. Bannister
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  3. Lionel Tarassenko
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Clifton, D.A., Bannister, P.R., Tarassenko, L. (2006). Learning Shape for Jet Engine Novelty Detection. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_121

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  • DOI: https://doi.org/10.1007/11760191_121

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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